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Analysis of Student Members' Attitudes on Out-of-Curriculum Science Communication Activities and Resultant Educational Effects 2nd

Analysis of Student Members' Attitudes on Out-of-Curriculum ...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Hasegawa, Makoto Chitose Inst Sci & Technol 758-65 Bibi Chitose Hokkaido 0668655 Japan

ISBN: (纸本)9783030402747;9783030402730

The out-of-curriculum project team "Rika-Kobo" organized by university students has actively performed various science communication activities in local community that mainly aim to stimulate interests of children and other generations into various fields of sciences and technologies for over 15 years. The activities of the project team are in out-of-curriculum basis. This means that the student members are not extrinsically motivated, but instead voluntarily involved in the activities. As a result, they are likely to have the same level of motivations and enthusiasms, leading to generally high qualities in the activities. As the supervisor of the activities, the author believes that the out-of-curriculum situation of the activities is critical for realizing such successful performances. For investigating actual attitudes of the student members towards involvement in the activities, questionnaires were provided in which among other questions, the student members were asked whether or not they were in favor of the situation of incorporating the activities into official education curriculum of the university so that the student members can get credits and/or improve their GPA scores through their participation in the activities. The obtained results showed that the majority of the student members believed that the activities should remain in the out-of-curriculum basis, similarly to the author's opinion as the supervisor of the activities. Interestingly, the student members in higher grades (in other words, with longer period of experiences in the activities) were likely to be in more strongly favor of the out-of-curriculum situation. Longer period of participation in the activities makes the student members further believe that their intrinsic motivation is a key factor for achieving satisfactory and successful performances and results.

关键词： Project-Based-learning PBL Student program Science education engineering education Science communication Career development

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Work-in-progress: Industry 4.0 production line for educational use: Multi stage production plant and interactive ar model 2nd

Work-in-progress: Industry 4.0 production line for education...

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22nd international conference on Interactive Collaborative learning, ICL 2019

作者： Hauß, Robert Schachinger, Gabriele Kalteis, Gerald Technical Secondary College of Engineering TGM Wexstraße 19-23 Vienna1200 Austria

ISBN: (纸本)9783030402730

At federal secondary colleges of engineering (HTL) in Austria technical education is taught at a quite high level of ISCED 5. Despite mechanical engineering, design with industrial standard 3D programs being state of the art in industry and education, technologies using Internet of Things (IoT) and Augmented Reality (AR) have been established in the last year. This publication describes the introduction of a multistage production and assembly line on an Industry 4.0 level using the IoT platform ThingWorx. For the plant an AR model has been established where the digital twin of all products can be observed in real time. © Springer Nature Switzerland AG 2020.

关键词： Internet of things

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learning Inertial Measurement Error Compensation in GPS Signal Shielded Using LSTM

Learning Inertial Measurement Error Compensation in GPS Sign...

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ACIS international conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Yu-Fan Wu Guo-Shing Huang Ming-Cheng Kao Institute of Electronic Engineering National Chin-Yi University of Technology Taichung City Taiwan Hsiuping University of Science and Technology Taichung City Taiwan

ISBN: (纸本)9781665447324

GPS (Global Positioning System) is an indispensable technology in vehicle positioning and navigation. Now the GPS positioning technology is very mature and is developing towards high-precision and high-reliability technology. However, the stability of GPS needs to be improved. This paper uses RTK (Real Time Kinematic) real -time dynamic differential positioning technology that can improve GPS accuracy, as well as basic simple inertial navigation components such as gyroscopes, accelerometers, and magnetic compasses as GPS. Compensation during interruption improves the reliability of GPS positioning. However, the error of the long-term inertial navigation system accumulates over time, which seriously affects the navigation accuracy, and the accuracy of the simple sensor output is not high. Therefore, this paper proposes a neural network-like learning scheme that uses LSTM to achieve high-precision and reliable positioning. We use cars to collect the XY position coordinate data of the original vehicle around the urban area without difference and with difference positioning. Use MATLAB offline operation to calculate λ (longitude), Φ (latitude) and use the data measured by integrated inertial elements to assist navigation in the road section that is shielded by GPS signals. And use LSTM deep learning to correct its errors, and then compare with and without differential positioning methods to get a more optimized path map to achieve the compensation effect.

关键词： Software algorithms Kinematics Inertial navigation Magnetic noise Real-time systems Sensors Reliability

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Flipped Classroom and Serious Games as a New learning Model in Experimental Sciences at the University 2nd

Flipped Classroom and Serious Games as a New Learning Model ...

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作者： Ouchaouka, Lynda Omari, Kamal Talbi, Mohammed Moussetad, Mohamed El Amrani, Najat Labriji, Lahoucine Univ Hassan II Casablanca Fac Sci Ben MSik Lab Ingn & Mat LIMAT Observ Rech Didact & Pedag Univ ORDIPU Av Driss El Harti Sidi OthmaneBP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik Dept Math & Informat Av Driss El Harti Sidi OthmaneBP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik Observ Rech Didact & Pedag Univ ORDIPU Av Driss El Harti Sidi OthmaneBP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik Observ Rech Didact & Pedag Univ ORDIPU Lab Ingn & Mat LIMAT FSBM Av Driss El Harti Sidi OthmaneBP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik Dept Biol Lab Biol & Sante LBS Av Driss El Harti Sidi OthmaneBP 7955 Casablanca Morocco

ISBN: (纸本)9783030402747;9783030402730

In this work, we present a new model of teaching based on flipped classroom and serious gaming in order to improve the efficiency of the training in experimental science at the university. We shall demonstrate the relevance of this model regarding the scientific disciplines particularly, the cell biology. The proposed study is based on a statistical investigation on the module of cell biology teached during the first year of undergraduate bachelor in the faculty of science Ben M'sick at Hassan II university of Casablanca. There is a consensus that both flipped classroom and serious games have a significant potential as tools for instruction and they must be used appropriately in pedagogical and learning strategies. However, their effectiveness in terms of learning outcomes are still understudied mainly due to the complexity involved in building, managing and assessing.

关键词： Flipped classroom Serious game Game design Pedagogical engineering Assessment

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The Vital Role of Small and Marginal Farmer in Future of Our Climate: Democratization of Machine learning, Artificial Intelligence, and Dairy Cow Necklace Sensors in Achieving the UN Climate Change Goals (COP21) and the Paris Agreement 25th

The Vital Role of Small and Marginal Farmer in Future of Our...

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Vuppalapati, Chandrasekar Ilapakurti, Anitha Vissapragada, Sandhya Mamidi, Vanaja Vuppalapati, Sriya Vuppalapati, Akshay Kedari, Sharat Kedari, Santosh Vuppalapati, Jaya Kallakur, Karthik Rajasri, Koduru Lella, Narendra San Jose State University San Jose United States Hanumayamma Innovations and Technologies Inc. Fremont United States Hanumayamma Innovations and Technologiesa PVT Limited Hyderbad India

ISBN: (纸本)9783031859229

Small and marginal dairy farmers are a crucial part of the global farming community, and there are approximately 570 million farms worldwide, with the majority being small farms, as per the Food and Agriculture Organization (FAO). The international Farm Comparison Network (IFCN) estimates that there are around 133 million dairy farms globally. Despite being a significant source of income for small dairy farmers, they often face challenges regarding cattle productivity and health issues. Interestingly, cattle rumination counts could be the solution to these issues, not only for small farmers but also for the world at large. Cattle rumination count is a measure of how much time cows spend chewing cud, which is a mixture of partially digested food regurgitated from the cow’s stomach to the mouth and further broken down by chewing before being swallowed again. Rumination count is a critical indicator of a cow’s health and productivity since cows that chew more tend to have better digestion, absorb more nutrients from their feed, and produce more milk. Cattle rumination count is also related to methane emissions, as cows release methane gas during the digestive process, especially during rumination. Methane is a potent greenhouse gas that contributes to climate change. By using Dairy IoT Cattle Cow Necklace sensors, small and marginal farmers worldwide can track rumination counts proactively. The Dairy Cow Necklace sensor records the time spent chewing cud, and this data can be used by small farmers and veterinarians to identify cows experiencing digestive issues or other health problems and adjust their feeding and management practices accordingly. Reducing methane emissions from dairy cattle is a vital part of global efforts to mitigate climate change since livestock, including dairy cattle, contributes to around 14.5% of global greenhouse gas emissions. In a recent study, rumination counts were captured from three major dairy producing areas in India (Vishakhapatnam,

关键词： COP21 Cow Necklace Sensor Methan emissions Rumination

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Real-time RADAR and LIDAR sensor fusion for automated driving 1

引用

2nd international conference on Machine Intelligence and Signal Processing, MISP 2019

作者： Devagiri, Rohini Iyer, Nalini C. Maralappanavar, Shruti School of Electronics and Communication Engineering KLE Technological University Hubli India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

In this paper, an attempt to detect obstacle using fusion of RADAR and LIDARdata is being made using Kalman filter. Kalman filter is the preferred algorithm for multi-sensor data fusion as it is computationally efficient compared to deep learning techniques. The algorithm emphasizes on widening the field of view in comparison with individual sensors and corrects failure of either of the sensors. Observation and measurement noise covariance data for Kalman filter is computed using the operating range of sensors and real-time sensor data, respectively. The proposed fusion algorithm has been tested on a real vehicle and verified for different region of operations of both RADAR (AWR1642) and LIDAR (CE30) sensors and the comparison is reported. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Kalman filters

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Context-Aware learning for Anomaly Detection with Imbalanced Log data 22

Context-Aware Learning for Anomaly Detection with Imbalanced...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Sun, Peijie Yuepeng, E Li, Tong Wu, Yulei Ge, Jingguo You, Junling Wu, Bingzhen Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China University of Exeter College of Engineering Mathematics and Physical Sciences ExeterEX4 4QF United Kingdom

ISBN: (纸本)9781728176499

Logs are used to record runtime states and significant events for a software system. They are widely used for anomaly detection. Logs produced by most of the real-world systems show clear characteristics of imbalanced data because the number of samples in different classes varies sharply. The distribution of imbalanced data makes the anomaly classifier bias toward the majority class, so it is difficult for a classifier to learn to detect anomalies correctly. Most existing methods for log-based anomaly detection ignore this important problem, so they perform poorly on real-world systems. In this paper, we propose a context-Aware method named AllContext for anomaly detection with imbalanced log data. AllContext transforms each log event into a vector, which contains not only the semantic information of each word, but also the semantics of the region where each word is located. Such rich semantic information enables our method to understand the imbalanced log data better and deeper. We conduct extensive experiments on multi-class and binary imbalanced log datasets. The accuracy of the proposed AllContext is more than twofold of that by a baseline state-of-The-Art. To evaluate the robustness of the proposed method, we assess AllContext on the imbalanced unseen log data, where all samples in the test dataset do not exist in the training dataset, and the accuracy achieved by AllContext reaches 0.98. The experiments show that the proposed solution achieves accurate results on both imbalanced and unseen log data. © 2020 IEEE.

关键词： Anomaly detection

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Early Alzheimer’s Detection Using Bidirectional LSTM and Attention Mechanisms in Eye Tracking 22nd

Early Alzheimer’s Detection Using Bidirectional LSTM and A...

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22nd international conference on Scientific Computing and Bioinformatics, CSC 2024, and 25th international conference on Computational Biology, BIOCOMP 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Ghayoumi, Mehdi Ghazinour, Kambiz Ghayoumi, Mehrad CyberSecurity Department SUNY at Canton CantonNY United States ALLGalaxy Los AngelesCA United States

ISBN: (纸本)9783031859014

This study introduces a deep-learning framework aimed at improving the early detection of Alzheimer’s Disease (AD) through the analysis of eye movement patterns. We developed a Bidirectional Long Short-Term Memory (Bi-LSTM) network enhanced with an attention mechanism, utilizing a dataset consisting of eye movement data from both early-stage AD patients and a control group. The model captures temporal dynamics and ocular characteristics that may indicate early cognitive decline. Our empirical results show that the Bi-LSTM network with attention mechanism performs better than traditional models in metrics such as accuracy, precision, recall, F1 score, and the area under the Receiver Operating Characteristic (ROC) curve. These findings suggest that eye movement data could be a useful, non-invasive tool for early AD detection. The study highlights the potential for more accessible and timely diagnostic methods, which could support earlier intervention and better patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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MTLH: Video QoE Monitoring for Encrypted Traffic via Multi-Task learning with Hierarchy 22

MTLH: Video QoE Monitoring for Encrypted Traffic via Multi-T...

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作者： Xu, Lei Xiong, Gang Gou, Gaopeng Chen, Jie Cui, Tianyu Shan, Tianyi Institute of Information Engineering Chinese Academy of Sciences Beijing China University of California San Diego United States

ISBN: (纸本)9781728176499

With the continuous growth of video traffic on the whole Internet, it is of vital importance for content providers to provide high-quality service. Monitoring the QoE (Quality of Experience) of video can help them to improve their service. However, most of the video traffic is encrypted traffic which has unreadable payload and less information in the transport layer that makes monitoring QoE a challenging task. QoE is measured from multiple metrics and many methods have been proposed to infer it. Traditional machine learning based methods have two main shortcomings. 1) These methods ignore the relevance of these metrics which can do a favor to the inference of QoE. 2) They need to extract the transport layer features and statistic features manually. In this paper, we propose multi-Task learning with hierarchy (MTLH) model for inferring video QoE metrics. We use shared layer to extract deep features automatically from traffic tendency pictures which reveal the variation tendency of video streaming, that characterizes video QoE. In addition, we utilize the hierarchy to promote fine-grained start-up delay tasks. Finally, we use multitask learning architecture to learn the correlation between each task and use their correlation to refer QoE metrics. We evaluate our model for YouTube adaptive video streams on two datasets. The proposed approach achieved an average 9.0% higher of precision score and an average 11.5% higher of recall score than the state-of-The-Art method in two datasets. © 2020 IEEE.

关键词： Quality of service

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Teaching Web Programming Using the MEAN Stack 2nd

Teaching Web Programming Using the MEAN Stack

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作者： Gomes, Luis Mendes Martins, Francisco Guerra, Helia NIDeS Univ Azores Ponta Delgada Portugal Ctr Algoritmi Univ Minho Braga Portugal Univ Lisbon LASIGE Fac Sci Lisbon Portugal

ISBN: (纸本)9783030402716;9783030402709

Web programming is a mandatory course in the syllabus of any BSc degree in Informatics engineering. But, nowadays, designing and teaching an up-to-date web programming course is a very demanding challenge due to the fast technological advances that imposes a continuous update of the concepts, techniques, and tools. Nowadays, from a professional perspective, web development is starting to converge on merging front-end and back-end development. This requires new skills for web developers, traditionally separated into two groups (front-end and back-end), that are still not accompanied by academia web programming courses. This future trend motivates us for designing a higher education course that combines Web design and server-side programming skills in a unified full-stack of Web technologies to develop modern web applications. The paper intends to present and discuss a new higher education course for a curricular unit in Web programming of a degree in Informatics in the European higher education system. The course is aligned with the new trends, providing students with fundamentals and basic skills in the development of Web applications, using the full-stack approach to Web development supported by the MEAN stack architecture.

关键词： Higher education BSc Informatics engineering Web programming Full-stack MEAN architecture

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