检索结果-内蒙古大学图书馆

8th International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2020

作者： Fidalgo-Blanco, Ángel Sein-Echaluce, María Luisa García-Peñalvo, Francisco José Dept. Geological and Mining Engineering Technical University of Madrid Madrid Spain Dept. Applied Mathematics University of Zaragoza Zaragoza Spain Research Institute for Educational Sciences Computer Science Department University of Salamanca Salamanca Spain

ISBN: (纸本)9781450388504

One of the first measures to fight against the COVID-19 pandemic was the confinement of the society and, consequently, the impossibility of providing presence-based training. For this reason, the faculty had to change the training to a completely online model, in a very short period of time and without the possibility of planning the process of change. The COVID-19 remains in our lives and continues to affect the training model. Currently, there are teachers using online training, face-to-face classes and there are even dual models where the faculty teaches both presence-based and online classes at the same time. For this reason, there is a need for training methods capable of adapting to different situations. In this work the Flipped Classroom model (a method used in face-to-face classes to make students more active) has been adapted to a hybrid model which can be implemented in any situation at any time. The model was applied during confinement in programming laboratories and the impact of this model has been measured through the works generated by students during the continuous evaluation. The result has been highly positive because the number of works presented by students the generated academic doubts have increased, as well as the average grades obtained during the continuous evaluation. © 2020 ACM.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Towards Arabic Multimodal Dataset for Sentiment Analysis

Towards Arabic Multimodal Dataset for Sentiment Analysis

引用

Intelligent Data science Technologies and Applications (IDSTA), International Conference on

作者： Abdelhamid Haouhat Slimane Bellaouar Attia Nehar Hadda Cherroun Lab. d'Informatique et de Mathématiques Université Amar Telidji Laghouat Algérie Dept. of Mathematics and Computer Science Lab. des Mathématiques et Sciences Appliquées (LMSA) Université de Ghardaia Algérie Université Ziane Achour Djelfa Algérie Lab. d'Informatique et de Mathématiques (LIM) Université Amar Telidji Laghouat Algérie

Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are central to almost all human activities and are key influencers of our behaviors. In addition, the recent deployment of Deep Learning-based (DL) models has proven their high efficiency for a wide range of Western languages. In contrast, Arabic DL-based multimodal sentiment analysis (MSA) is still in its infantile stage due, mainly, to the lack of standard datasets. In this paper, our investigation is twofold. First, we design a pipeline that helps building our Arabic Multimodal dataset leveraging both state-of-the-art transformers and feature extraction tools within word alignment techniques. Thereafter, we validate our dataset using state-of-the-art transformer-based model dealing with multimodality. Despite the small size of the outcome dataset, experiments show that Arabic multimodality is very promising.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

Performance Evaluation of Popular Deep Neural Networks for N...

引用

Frontiers of Information Technology (FIT)

作者： Muhammad Naeem Abu Bakar Siddique Raja Hashim Ali Usama Arshad Zain ul Abideen Talha Ali Khan Muhammad Huzaifa Shah Ali Zeeshan Ijaz Nisar Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging AI and NLP in Chatbot Development: An Experimental Study

Leveraging AI and NLP in Chatbot Development: An Experimenta...

引用

Frontiers of Information Technology (FIT)

作者： Abdul Wahab Paracha Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Faculty of Electronic Systems Engineering University of Regina Regina Canada

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model’s performance indicates a considerable dependence on the dataset’s size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Radio-Frequency Pseudo-Null Induced by Light in an Ion Trap

arXiv

引用

arXiv 2025年

作者： Chung, Daun Cha, Yonghwan Shon, Hosung Park, Jeonghyun Lee, Woojun Lee, Kyungmin Cho, Beomgeun Choi, Kwangyeul Kim, Chiyoon Yoo, Seungwoo Kim, Suhan Jeong, Uihwan Kang, Jiyong You, Jaehun Kim, Taehyun Dept. of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

In a linear radio-frequency (rf) ion trap, the rf null is the point of zero electric field in the dynamic trapping potential where the ion motion is approximately harmonic. When displaced from the rf null, the ion is superimposed by fast oscillations known as micromotion, which can be probed through motion-sensitive light-atom interactions. In this work, we report on the emergence of the rf pseudo-null, a locus of points where the ion responds to light as if it were at the true rf null, despite being displaced from it. The phenomenon is fully explained by accounting for the general two-dimensional structure of micromotion and is experimentally verified under various potential configurations, with observations in great agreement with numerical simulations. The rf pseudo-null manifests as a line in a two-dimensional parameter space, determined by the geometry of the incident light and its overlap with the motional structure of the ion. The true rf null occurs uniquely at the concurrent point of the pseudo-null lines induced by different light sources. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Temporal Relevance Analysis for Video Action Models

arXiv

引用

arXiv 2022年

作者： Fan, Quanfu Kim, Donghyun Chen, Chun-Fu Sclaroff, Stan Saenko, Kate Bargal, Sarah Adel Image and Video Computing Dept. of Computer Science Boston University United States MIT-IBM Watson AI Lab United States Future Lab for Applied Research and Engineering JPMorgan Chase Bank N.A United States

In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to quantify the temporal relationships between frames captured by CNN-based action models based on layer-wise relevance propagation. We then conduct comprehensive experiments and in-dept. analysis to provide a better understanding of how temporal modeling is affected by various factors such as dataset, network architecture, and input frames. With this, we further study some important questions for action recognition that lead to interesting findings. Our analysis shows that there is no strong correlation between temporal relevance and model performance;and action models tend to capture local temporal information, but less long-range dependencies. Our codes and models will be publicly available. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

***: Redefining Creative Play with Human-AI Competition in Real-Time Doodle Recognition

引用

Systems and Soft Computing 2025年

作者： Ishan Panta Biplov Paneru Bishwash Paneru Bipul Thapa Department of computer science and Engineering Kathmandu University Nepal Dept. of Electronics and Communication Engineering Nepal Engineering College affiliated to Pokhara University Nepal Dept. of Applied Sciences and Chemical Engineering Institute of Engineering Tribhuvan University Nepal Dept of Computer Science and Engineering Kathmandu University Dhulikhel Nepal

*** is an innovative web-based game that takes creativity, guessing, and artificial intelligence and puts them together in a great combination. It is both an entertaining and educational game, enabling players to enhance their creativity and guessing skills while exploring AI capabilities interactively. The participants draw random words as they compete with an artificial intelligence model, which predicts drawings in real time. The AI component not only adds challenge and thrill but also demonstrates the capability of machine learning in interactive gaming. To create a highly precise doodle recognizer, a Long-term Recurrent Convolutional Network (LRCN) was employed, and it proved to be much better than the typical Convolutional Neural Networks (CNNs). The LRCN model achieved 90% accuracy in the top-1 score and 97% in the top-3 score, which is a measure of its high level of prediction. Relative to CNNs, which were overfitting (training loss: 36%, validation loss: 53%), LRCN demonstrated a substantial learning curve, stabilizing at a final loss of 39%, thereby offering better resilience. Furthermore, LRCN outperformed CNN, and therefore particularly has been found useful in dynamic gaming conditions. *** is more than just for fun, providing interesting commentary on the domain of AI-based sketch recognition and the viability of deep learning algorithms in real time. The wide variety of categories and questions in the game makes it an enjoyable experience, as users are challenged to sharpen their creativity and intellectual skills while engaging with advanced AI technologies.

关键词： Machine Learning Long-term Recurrent Convolutional Network Online game Pattern Recognition

来源：评论

学校读者我要写书评

暂无评论

Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

引用

Frontiers of Information Technology (FIT)

作者： Hassan Ashfaq Usama Arshad Raja Hashim Ali Zain ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model’s performance. To address this, we propose using regularization and data augmentation techniques to enhance the model’s generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Combining Survival Analysis and Simheuristics to Predict the Risk of Delays in Urban Ridesharing Operations with Random Travel Times 22

Combining Survival Analysis and Simheuristics to Predict the...

引用

Proceedings of the Winter Simulation Conference

作者： Erika M. Herrera Javier Panadero Angel A. Juan Patricia Carracedo Elena Perez-Bernabeu Rocio de la Torre Universitat Oberta de Catalunya Computer Science Department Barcelona SPAIN Universitat Politècnica de València Dept. of Applied Statistics and Operations Research Plaza Ferrandiz-Carbonell Alcoy SPAIN Universitat Politècnica de València Department of Business Organization Plaza Ferrandiz-Carbonell Alcoy SPAIN

More sustainable transportation and mobility concepts, such as ridesharing, are gaining momentum in modern smart cities. In many real-life scenarios, travel times among potential customers' locations should be modeled as random variables. This uncertainty makes it difficult to design efficient ridesharing schedules and routing plans, since the risk of possible delays has to be considered as well. In this paper, we model ridesharing as a stochastic team orienteering problem in which the trade-off between maximizing the expected reward and the risk of incurring time delays is analyzed. In order to do so, we propose a simulation-optimization approach that combines a simheuristic algorithm with survival analysis techniques. The aforementioned methodology allows us to generate not only the probability that a given routing plan will suffer a delay, but also gives us the probability that the routing plan experiences delays of different sizes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DarSIA: An open-source Python toolbox for two-scale image processing of dynamics in porous media

arXiv

引用

arXiv 2023年

作者： Nordbotten, Jan Martin Benali, Benyamine Both, Jakub Wiktor Brattekås, Bergit Storvik, Erlend Fernø, Martin A. Center for Modeling of Coupled Subsurface Dynamics Dept. of Mathematics University of Bergen Norway Norwegian Research Center Postboks 22 Nygårdstangen Bergen5838 Norway Department of Physics and Technology University of Bergen Norway Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied Sciences Norway

Understanding porous media flow is inherently a multi-scale challenge, where at the core lies the aggregation of pore-level processes to a continuum, or Darcy-scale, description. This challenge is directly mirrored in image processing, where grains and interfaces may be clearly visible, yet continuous parameters are desirable to measure. Classical image processing is poorly adapted to this setting, as most techniques do not explicitly utilize the fact that the image contains explicit physical processes. Here, we adapt classical image processing concepts to what we define as "physical images" of porous materials and processes within them. This is realized through the development of a new open-source image analysis toolbox specifically adapted to time-series of images of porous materials. © 2023, CC BY.

关键词： Porous materials

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：