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23rd International Conference on Computational Science and Its Applications, ICCSA 2023

作者： Stabile, Federica Garau, Chiara Rossetti, Silvia Torrisi, Vincenza Department of Engineering and Architecture University of Parma Parco Area delle Scienze 181/A Parma43124 Italy Department of Civil and Environmental Engineering and Architecture University of Cagliari Cagliari09129 Italy Department of Electrical Electronic and Computer Engineering University of Catania Catania95125 Italy

ISBN: (纸本)9783031371226

Improve walkability, and therefore the capacity to provide safe and accessible walking opportunities, has emerged as a crucial topic especially for post-pandemic cities, seeking to create inclusive and liveable urban spaces. In this direction, a user-centred planning approach prioritises the creation of pleasant and accessible walking environments, which are essential for achieving a sustainable urban development. The physical features of the urban areas must be carefully designed and implemented to enhance the living standards, safety, and inclusiveness, also for vulnerable users. Based on the Geographic Information System (GIS) tools, this paper provides an analytical-assessment approach for evaluating walkability in urban settings, in order to identify the significant properties of urban infrastructures. The methodology is characterised by an integrated approach based on a survey process, including remote observations. The evaluation of pedestrian paths’ performances considers the most influencing attributes (e.g., sidewalk width;visibility;crosswalk provision) which have been identified and evaluated for the case study of Largo Felice in Cagliari, Italy. To obtain an overall assessment in terms of safety, approachability, and enjoyment, each of these indicators has been analysed by aggregating and cross-referencing the detected attributes. The results highlight critical issues associated with the study area, that hinder the approachability and safety of pedestrian pathways, e.g., architectural barriers, discontinuities, poor visibility. Finally, the paper provides some recommendations for improving walkability in urban areas. Future research developments will include the assessment of further walkability attributes to perform a comprehensive analysis of an extended study area and in-field survey to validate the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Visibility

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A Transfer Learning Approach with Modified VGG 16 for Driving Behavior Detection in Intelligent Transportation Systems 6

A Transfer Learning Approach with Modified VGG 16 for Drivin...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Nur, Abdullah Hafez Hasan Talukder, Md Simul Adnan, Shihab Ahmed, Md. Razu International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Bangladesh Atomic Energy Regulatory Authority Nuclear Safety Security and Safeguard Division Dhaka Bangladesh International Islamic University Chittagong Dept. of Computer and Communication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

The majority of driving errors resulting from poor driving habits, a lack of compliance with the law, and inadequate driving knowledge continue to be a major global problem for public safety. Delivering accurate and timely warnings to drivers for correction while also allowing law enforcement authorities to catch violators is a critical task. To address this problem, the paper examines the complex aspects of human behavior and the different variables that cause distractions on the road, and a highly reliable and robust transfer-learning-based AI-system has been proposed. Initially, a novel dataset with ten types of driving behaviors was developed. Afterward, a fine-tuned modified VGG16-model was applied to classify them. In addition, the model was trained by assessing the efficacy of four optimizers: Adam, SGD, AdaGrad, and RMSProp. The optimized modified VGG16 with the Adam Optimizer proved to be the best, attaining excellent accuracy, recall, F1 score, and precision, all of which were 99.86%. It illustrates the effectiveness of the proposed approach in significantly improving the accuracy of identifying distracted driver- behavior. © 2024 IEEE.

关键词： Roads and streets

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Commercial Clustering of Indian Bamboo Species Using Machine Learning Techniques 2

Commercial Clustering of Indian Bamboo Species Using Machine...

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2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Sawarkar, Ankush D. Shrimankar, Deepti D. Sahu, Santosh Kumar Singh, Lal Bokde, Neeraj Dhanraj Kumar, Manish Department of Computer Science and Engineering Maharashtra Nagpur440 010 India Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Maharashtra Nagpur440 013 India National Environmental and Engineering Research Institute Environmental Biotechnology and Genomics Division Maharashtra Nagpur440 020 India Aarhus University Center for Quantitative Genetics and Genomics Aarhus Denmark Palermo University Engineering Department Viale delle Scienze Ed.8 Palermo90128 Italy

ISBN: (纸本)9798350310719

Bamboo is a grass that grows quickly (lm/days) and is very important to India's social, economic, and environmental growth. India has a big market for bamboo because of the gap between its imports (3,306 thousand $) and exports (607 thousand $) of its products. Clustering bamboo for business will be important in the near future since it can be used in over 1,000 ways and is worth USD 2.969 billion in trade. Machine learning (ML) clustering models play a vital role in achieving this task for the commercial clustering of bamboo. In this research, we have presented details of twenty commercial species of bamboo in India has been identified, and data on the 2000 bamboo species have been collected. Although many algorithms have been introduced for clustering in recent years, not on bamboo, especially on morphological data. The target of this paper is to cluster these different bamboo species based on its commercial value using various ML algorithms such as K-means, Gaussian Mixture Models (GMM), and Balance Iterative Reducing and Clustering using Hierarchies (BIRCH). Clustering comes under unsupervised learning;there is no direct accuracy count. To evaluate the performance of clustering and determine which algorithm is best for clustering, we have used other indirect performance measures such as Silhouette Score, Calinski-Harabasz Index (CHI), and the Davies-Bouldin Index (DBI). K-mean shows the highest performance measures among all the other clustering ML models, with achieve a silhouette score of 0.5126, CHI of 17315 and DBI of 0.6633. © 2023 IEEE.

关键词： Bamboo

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LLM4CP:Adapting Large Language Models for Channel Prediction

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Journal of Communications and Information Networks 2024年第2期9卷 113-125页

作者： Boxun Liu Xuanyu Liu Shijian Gao Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and TechnologyHong KongChina

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

关键词： channel prediction massive multi-input multi-output(m-MIMO) large language models(LLMs) fine-tuning time-series

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Enhancing Network Security Management in Water Systems using FM-based Attack Attribution

arXiv

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arXiv 2025年

作者： Avdalović, Aleksandar Khoury, Joseph Taha, Ahmad Bou-Harb, Elias Division of Computer Science and Engineering Louisiana State University United States Civil and Environmental Engineering Vanderbilt University United States

Water systems are vital components of modern infrastructure, yet they are increasingly susceptible to sophisticated cyber attacks with potentially dire consequences on public health and safety. While state-of-the-art machine learning techniques effectively detect anomalies, contemporary model-agnostic attack attribution methods using LIME, SHAP, and LEMNA are deemed impractical for large-scale, interdependent water systems. This is due to the intricate interconnectivity and dynamic interactions that define these complex environments. Such methods primarily emphasize individual feature importance while falling short of addressing the crucial sensor-actuator interactions in water systems, which limits their effectiveness in identifying root cause attacks. To this end, we propose a novel model-agnostic Factorization Machines (FM)-based approach that capitalizes on water system sensor-actuator interactions to provide granular explanations and attributions for cyber attacks. For instance, an anomaly in an actuator pump activity can be attributed to a top root cause attack candidates, a list of water pressure sensors, which is derived from the underlying linear and quadratic effects captured by our approach. We validate our method using two real-world water system specific datasets, SWaT and WADI, demonstrating its superior performance over traditional attribution methods. In multi-feature cyber attack scenarios involving intricate sensor-actuator interactions, our FM-based attack attribution method effectively ranks attack root causes, achieving approximately 20% average improvement over SHAP and LEMNA. Additionally, our approach maintains strong performance in single-feature attack scenarios, demonstrating versatility across different types of cyber attacks. Notably, our approach maintains a low computational overhead equating to an O(n) time complexity, making it suitable for real-time applications in critical water system infrastructure. Our work underscores the impor

关键词： Factorization

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Mechanistic modeling of copper corrosions in data center environments

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Building Simulation 2024年第3期17卷 483-492页

作者： Rui Zhang Jianshun Zhang Roger Schmidt Jeremy L.Gilbert Department of Mechanical and Aerospace Engineering College of Engineering and Computer ScienceSyracuse UniversitySyracuseNY13244USA Buildings and Transportation Science Division Oak Ridge National LaboratoryOak RidgeTN37830USA SyracuseCoE-New York Center of Excellence in Environmental and Energy Systems Syracuse University727 E.Washington StreetSyracuseNY13244USA Department of Bioengineering Clemson UniversityClemson-Medical University of South Carolina Bioengineering ProgramCharlestonSC29425USA

Air-side economizers are increasingly used to take advantage of“free-cooling”in data centers with the intent of reducing the carbon footprint of ***,they can introduce outdoor pollutants to indoor environment of data centers and cause corrosion damage to the information technology *** evaluate the reliability of information technology equipment under various thermal and air-pollution conditions,a mechanistic model based on multi-ion transport and chemical reactions was *** model was used to predict Cu corrosion caused by Cl_(2)-containing pollutant *** also accounted for the effects of temperature(25℃and 28℃),relative humidity(50%,75%,and 95%),and *** also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator,which agreed well with the experimental *** average root mean square error of the prediction was 13.7Å.The model can be used to evaluate the thermal guideline for data centers design and operation when Cl_(2)is present based on pre-established acceptable risk of corrosion in data centers’environment.

关键词： chloride pollutant mixture copper corrosion model data center temperature relative humidity synergistic effect

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FUCOM-Kendall Model for Evaluating E-Scooter Adoption: A Case Study in Catania, Italy

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Transportation Research Procedia 2025年 86卷 1-8页

作者： Vincenza Torrisi Rosalia Camporeale Sarbast Moslem Tiziana Campisi Department of Electric Electronic and Computer Engineering Catania Italy Department of Technology and Society Division of Transport and Roads Lund Sweden School of Architecture Planning and Environmental Policy University College Dublin Ireland Faculty of Engineering and Architecture University of Enna KORE Enna Italy

Electric scooters (e-scooters) have emerged as a fundamental component of urban micro-mobility, particularly in European cities during the Covid-19 pandemic. This study employs the Full Consistency Method (FUCOM) and Kendall rank correlation method to assess the preferences and priorities of university students in Catania, Italy, regarding e-scooter adoption. By analyzing influencing factors, this research offers a comprehensive understanding of student attitudes towards e-scooters and their adoption, contributing to strategies for enhancing sustainable urban mobility. The findings highlight significant preferences for factors such as parking ease, portability, and intermodality, while also identifying safety concerns and the need for better integration with public transport. These insights underscore the necessity for targeted improvements in e-scooter infrastructure and policies, aiming to optimize urban mobility and meet the specific needs of potential users. Further research will focus on comparative analysis across different urban settings, investigating addition user-centric factors to capture a more comprehensive view of e-scooter adoption.

关键词： e-scooter multi-criteria decision making FUCOM method Kendall rank correlation urban mobility

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Two-Operation Buffering based on Dynamic Optical RAM 17

Two-Operation Buffering based on Dynamic Optical RAM

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2024

作者： Semenov, Andriy Tsyrulnyk, Serhii Baraban, Serhii Semenova, Olena Rudyk, Andrii Khloba, Anton Vinnytsia National Technical University Faculty of Information Electronic Systems Vinnytsia Ukraine Vinnytsia National Agrarian University Department of Computer Sciences and Digital Economy Vinnytsia Ukraine Poznan University of Technology Faculty of Computing and Telecommunications Poznan Poland National University of Water and Environmental Engineering Department of Automation Electrical Engineering and Computer-Integrated Technologies Rivne Ukraine

ISBN: (纸本)9798331520564

The authors of this paper propose a block diagram for a dual-operating buffer on a dynamic optical RAM. The buffer consists of two dynamic optical RAMs, each controlled separately to allow for simultaneous writing and reading of data, thereby improving performance. This setup enables data to be read from the buffer while new data is being written to it. Implementing a dual-operating buffer on a dynamic optical RAM has the potential to enhance the performance of high-speed data transmission in telecommunications networks. The authors conducted numerical calculations to compare failure probabilities between single-operation and dual-operation buffers on dynamic optical RAM. The results indicated that using a dual-operating buffer significantly reduces the probability of failure with the same buffer memory capacity compared to a single-operating buffer. Additionally, the authors analyzed the effectiveness of the proposed dual-operating buffer. The analysis confirmed that exploiting dynamic optical memory for buffering enhances data transfer speed and efficiency due to the inherent properties of optical data transfer, such as high bandwidth and low latency. © 2024 IEEE.

关键词： Dynamic random access storage

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Multi-channel Avionics Module Fault Classification Based on Inception-CBAM Network 5

Multi-channel Avionics Module Fault Classification Based on ...

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5th International Conference on electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Yu, Lubin Peng, YiXi Han, Xingjian Liang, Shuqi Yang, Peiliang Liu, Junbin He, Shilie China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou China School of Software Engineering Sun Yat-Sen University Zhuhai China School of Computer Science and Technology Shandong University of Technology Zibo China

ISBN: (纸本)9798350386943

In today's highly integrated and automated aviation field, the stable operation of avionics systems is the foundation for maintaining flight safety and efficient operations. Because of the shortcomings in research on fault classification of avionics modules, this study proposes an Inception-CBAM network model, aiming to improve the accuracy and efficiency of fault diagnosis. This model combines the powerful multi-scale feature extraction capability of the Inception network with the attention mechanism of the CBAM module, which greatly enhances the ability to identify complex fault patterns. Experimental verification shows that the model can effectively classify various faults, enhance the scientificity and systematicness of diagnosis, and contribute important technical support to the formulation of aviation safety maintenance and fault prevention strategies. © 2024 IEEE.

关键词： Convolutional neural networks

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DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Based Event Argument Extraction with Slot Querying 31

DEGAP: Dual Event-Guided Adaptive Prefixes for Templated-Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Guanghui Liu, Dexi Nie, Jian-Yun Wan, Qizhi Hu, Rong Liu, Xiping Liu, Wanlong Liu, Jiaming School of Information Management Jiangxi University of Finance and Economics China Canada School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Statistics Division of the Physical Sciences The University of Chicago United States

ISBN: (纸本)9798891761964

Recent advancements in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference, such as retrieved instances and event templates. These methods face two challenges: (1) the retrieval results may be irrelevant and (2) templates are developed independently for each event without considering their possible relationship. In this work, we propose DEGAP to address these challenges through a simple yet effective components: dual prefixes, i.e. learnable prompt vectors, where the instance-oriented prefix and template-oriented prefix are trained to learn information from different event instances and templates. Additionally, we propose an event-guided adaptive gating mechanism, which can adaptively leverage possible connections between different events and thus capture relevant information from the prefix. Finally, these event-guided prefixes provide relevant information as cues to EAE model without retrieval. Extensive experiments demonstrate that our method achieves new state-of-the-art performance on four datasets (ACE05, RAMS, WIKIEVENTS, and MLEE). Further analysis shows the impact of different components. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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