检索结果-内蒙古大学图书馆

18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Cseminschi, Stanislav Ionica, Andreea Cristina Leba, Monica Florea, Vlad Alexandru University of Petrosani Department of Management and Industrial Engineering Petrosani Romania University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Mechanical Engineering Petrosani Romania

ISBN: (纸本)9789893347928

Stimulating creativity in technological entrepreneurial education leads to knowledge and connection to current trends in the development of innovative technological projects and the creation of innovative start-up and spin-off enterprises. Their success or failure depends on a number of factors such as the market, the team, the technological level of the product, but also on the funding methods, including crowdfunding. The paper proposes a Fuzzy tool for preliminary assessment of the opportunity of crowdfunding campaigns for innovative projects resulting from the stimulation of entrepreneurial creativity through gamification-based education. © 2023 ITMA.

关键词： engineering education

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for EEG-Based Motor Imagery Classification: Towards Enhanced Human-Machine Interaction and Assistive Robotics 9

Deep Learning for EEG-Based Motor Imagery Classification: To...

引用

9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Boutarfaia, Nejia Russo, Samuele Tibermacine, Ahmed Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Psychology Sapienza University of Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This study presents a comprehensive exploration of EEG-based motor imagery classification using advanced deep learning architectures. Focusing on six distinct motor imagery classes, we investigate the performance of convolutional neural networks (CNN), CNN with Long Short-Term Memory (CNN-LSTM), and CNN with Bidirectional LSTM (CNN-BILSTM) models. The CNN architecture excels with a remarkable accuracy of 99.86%, while the CNN-LSTM and CNN-BILSTM models achieve 98.39% and 99.27%, respectively, showcasing their effectiveness in decoding EEG signals associated with imagined *** results underscore the potential applications of this research in fields such as assistive robotics and automation, showcasing the ability to translate cognitive intent into robotic actions. This study offers valuable insights into the realm of deep learning for EEG analysis, setting the stage for advancements in brain-computer interfaces and human-machine interaction. © 2023 Copyright for this paper by its authors.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

The Dark Side of the Language: Pre-trained Transformers in the DarkNet

The Dark Side of the Language: Pre-trained Transformers in t...

引用

2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Ranaldi, Leonardo Nourbakhsh, Aria Patrizi, Arianna Ruzzetti, Elena Sofia Onorati, Dario Mastromattei, Michele Fallucchi, Francesca Zanzotto, Fabio Massimo Human-Centric ART Group Department of Enterprise Engineering University of Rome Tor Vergata Italy Department of Computer Control and Management Engineering University of Rome Sapienza Italy

ISBN: (纸本)9789544520922

Pre-trained Transformers are challenging human performances in many NLP tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In this paper, we explore how a range of pretrained Natural Language Understanding models perform on definitely unseen sentences provided by classification tasks over a DarkNet corpus. Surprisingly, results show that syntactic and lexical neural networks perform on par with pre-trained Transformers even after fine-tuning. Only after what we call extreme domain adaptation, that is, retraining with the masked language model task on all the novel corpus, pre-trained Transformers reach their standard high results. This suggests that huge pre-training corpora may give Transformers unexpected help since they are exposed to many of the possible sentences. © 2023 Incoma Ltd. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Human Mobility, AI assistants, and urban emissions: an insidious triangle

Human Mobility, AI assistants, and urban emissions: an insid...

引用

2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Pappalardo, Luca Böhm, Matteo Cornacchia, Giuliano Mauro, Giovanni Pedreschi, Dino Nanni, Mirco Pisa Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Computer Science University of Pisa Pisa Italy IMT Lucca Italy

Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Climate change

来源：评论

学校读者我要写书评

暂无评论

Sustainable business model in practice: a digital business game training for high school students 15

Sustainable business model in practice: a digital business g...

引用

15th International Conference on Society and Information Technologies, ICSIT 2024

作者： Fontanella, Sabrina Fraccascia, Luca Nonino, Fabio Scarnicchia, Alessandra Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Roma Lazio Italy Department of High-Tech Business and Entrepreneurship University of Twente Enschede Overijssel Netherlands

ISBN: (纸本)9781950492770

This paper explores the nexus between sustainable business models, education and technology, addressing pressing challenges in economic, social, and environmental spheres. On one hand, education is identified as a key tool for fostering sustainability principles and essential skills for future managers;on the other, businesses, particularly through sustainable business models (SBMs) and evolving digital platforms, play a pivotal role in advancing sustainability goals. The research answer to the need for sustainable development examining the potential of educational business games, blending entertainment and education to engage the 'gamer generation' actively. Considering the growing literature upon sustainable entrepreneurship and business models, the objective of the paper is to implement a digital business game for sustainability education designed to teach high school students how to implement an entrepreneurial activity through a sustainable business model. This study contributes to understanding sustainable business practices and innovative educational approaches, aligning with the global imperative for a sustainable future. In the end of the manuscript some prelaminar result of the game testing phase are presented. © Proceedings ICSIT, International Conference on Society and Information Technologies. All Rights Reserved.

关键词： Blending

来源：评论

学校读者我要写书评

暂无评论

Ontology-driven Data management Design in Healthcare Domain: The ADCATER Experience

Ontology-driven Data Management Design in Healthcare Domain:...

引用

2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Cocco, Leonardo Fantozzi, Paolo Lembo, Domenico Nanni, Umberto Scafoglieri, Federico Maria Department of Computer Control and Management Engineering University of Rome la Sapienza Rome00185 Italy Department of Law Economics Politics and Modern Languages LUMSA University Rome00192 Italy

In this paper, we outline our experience in implementing the data management component of a data-intensive healthcare application within the ADCATER project (Advanced Digital Solutions for Professional Food and Nutrition Catering Service), where an ad hoc ontology, tailored to the project’s domain, plays a crucial role in driving the system design. We will discuss the creation process of this ontology, its underlying building principles, and how it aids the development of a reconciled database useful to integrate and consolidate heterogeneous sources of information, vital for the proper running of solution at hand. Here, the ontology is essential for harmonizing vocabularies and ensuring the establishment of a schema devoid of inconsistencies. Finally, we will explore how Business Intelligence services, operating on the foundation of the Data Warehouse built upon the reconciled database, are seamlessly aligned with the crafted ontology. © 2024 Copyright for this paper by its authors.

关键词： Data warehouses

来源：评论

学校读者我要写书评

暂无评论

Differential gene correlation analysis to investigate evolutionary divergence between Cardamine hirsuta and Arabidopsis thaliana

Differential gene correlation analysis to investigate evolut...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： D'Annunzio, Federica Fiscon, Giulia Bertolotti, Gaia Dello Ioio, Raffaele Paci, Paola Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science 'A. Ruberti' National Research Council of Rome Department of Computer Control and Management Engineering 'A. Ruberti' Rome Italy Sapienza University of Rome Department of Biology and Biotechnology 'Charles Darwin' Rome Italy Department of Computer Control and Management Engineering 'A. Ruberti' Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Dissecting the intricate regulatory dynamics between genes stands as a critical step towards the development of precise predictive models within biological systems. A highly effective strategy in this pursuit involves an examination of variations in correlation among gene pairs across different conditions trough differential gene correlation analysis. We anticipated that the application of this method to phylogenetically close plant species could reveal powerful insights on how genes' relationships have a fundamental role in evolutionary dynamics. Building upon this principle, our investigation focused primarily on conducting a comprehensive differential correlation analysis of the roots' orthologous genes of two closely related plant species: Cardamine hirsuta and Arabidopsis thaliana. By leveraging their shared phylogenetic proximity, we were able to draw comparisons that shed light on how evolutionary pressures might have shaped their gene regulatory networks. By integrating differential expression analysis, differential correlation analysis and functional enrichment analysis, we offer a robust framework for future studies aiming to unravel the complexities of adaptation and evolution across a wide range of biological systems. © 2024 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

A network-based bioinformatic analysis for identifying potential repurposable drugs for obesity avoiding hepatic steatosis side-effect

A network-based bioinformatic analysis for identifying poten...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Brunetti, Martina Fiscon, Giulia Costanzo, Alessia Di Arca, Marcello Paci, Paola Sapienza University of Rome Department of Experimental Medicine Rome Italy Sapienza University of Rome Institute for Systems Analysis and Computer Science "a. Ruberti" National Research Council of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Sapienza University of Rome Policlinico Umberto I Department of Translational and Precision Medicine Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering "a. Ruberti" Rome Italy Karolinska Institutet Stockholm Sweden

ISBN: (纸本)9798350386226

Obesity is a complex multifactorial disorder characterized by the excess accumulation of body fat that impairs human health due to the risk of developing other diseases, including cardiovascular and hepatic diseases, hypertension, diabetes, hyperlipidemia. Its spread has been progressively accelerating, resulting in an unprecedented epidemic with no significant signs of slowing down any time soon. Drug therapy via the proposal repurposing solutions can represent an actionable treatment strategy, even if the emergence of drug-induced adverse effects can affect the treatment of this pathology. In this study, we propose a network-based analysis to identify a list of drug candidates predicted to be repurposable for obesity that are unlikely to produce specific adverse side-effect, such as hepatic steatosis. © 2024 IEEE.

关键词： Nutrition

来源：评论

学校读者我要写书评

暂无评论

CYBER-PHYSICAL SYSTEM IMPLEMENTATION FOR THE AR-ASSISTED AIT OF AEROSPACE COMPONENTS 34th

CYBER-PHYSICAL SYSTEM IMPLEMENTATION FOR THE AR-ASSISTED AIT...

引用

34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Pesce, J. Carini, A. Marinacci, M. Boscia, M. Marzioli, P. Pasquali, M. Eugeni, M. Mecella, M. Piergentili, P. Gaudenzi, P. Department of Mechanical and Aerospace Engineering Sapienza University of Rome Via Eudossiana 18 Rome00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Industry 4.0 technologies are profoundly transforming the assembly, integration, and testing (AIT) processes of aerospace components, with augmented reality (AR) and cyber-physical systems (CPS) at the forefront of this evolution, marking a significant shift in traditional methodologies. This research explores the integration of CPS into the AIT processes of aerospace components, leveraging AR as a powerful tool to enhance efficiency, precision, and safety. To do so, the case of an aerospace system that showcases the practical implementation of the proposed CPS and AR framework in aerospace AIT is presented. Results show how the developed CPS can provide valuable assistance to the AIT operator, constituting a promising option to achieve significant improvements in assembly accuracy and overall process efficiency. The research also explores the implications of the proposed framework on the testing phase of aerospace components resorting to the CPS capability to facilitate real-time monitoring of performance metrics, ensuring the accomplishment of stringent quality standards. © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：