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23rd DIDFYZ conference on New Approaches in Physics Education for Generation Z

作者： Houfková, J. Department of Physics Education Faculty of Mathematics and Physics Charles University V Holešovičkách 2 Praha 8 Czech Republic

Beyond the family, preschool teachers play a vital role in shaping children's attitudes towards exploration and inquiry, particularly in relation to the world around them and the field of science. This paper examines the attitudes of preschool teachers towards the significance of science education within the preschool curriculum. A comparative analysis is conducted between the perspectives of experienced in-service teachers and future teachers, with data gathered through questionnaires administered after their participation in science-focused teacher trainings. The study specifically focuses on physics experiments in preschool and examines the perspectives of both experienced teachers who have been instrumental in shaping the Alpha Generation, as well as future teachers who belong to the Z generation. Key findings revealed a discrepancy in familiarity with and perceptions of physics experiments between experienced teachers and future educators. Notably, it sheds light on the need for enhanced professional development programs to bolster science teaching skills among educators. By understanding the attitudes of preschool teachers, this research aims to identify areas for improvement and develop strategies to bolster the quality of science education in early childhood. This preliminary study serves as a foundation for further comprehensive analysis and emphasizes the significance of empowering educators to effectively integrate science into preschool curricula. © Published under licence by IOP Publishing Ltd.

关键词： Curricula

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Comparison of Heavy Rain Prediction with 1-hour and 2-hour Prediction Using Himawari-8 data Based on LSTM Algorithm 13

Comparison of Heavy Rain Prediction with 1-hour and 2-hour P...

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13th IEEE International conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Husnul, Nadia Harjupa, Wendi Ahmad, Umar Ali Budiman, Gelar Risyanto Telkom University School of Electrical Engineering Bandung Indonesia Center for Climate and Atmosphere National Research and Innovation Agency Brin Indonesia Kanazawa University Ishikawa Kanazawa Japan

ISBN: (纸本)9798350368086

Weather is one of the most unpredictable things in Indonesia. One of the factors is that Indonesia is an archipelago flanked by two large oceans, the Indian and Pacific Oceans. In addition, the fact that Indonesia is called the 'land of the equator' creates uncertainty and rapid changes in weather movements. So, the Meteorology, Climatology, and Geophysics Agency (BMKG) still have difficulty accurately predicting the weather. This paper uses the Deep Learning method for weather data analysis and processing. The deep learning algorithm is the Long-Short Term Memory (LSTM) algorithm, often used to process time series data. This paper proposes a combination of 13 rain detection parameters with a comparison of 1 -hour and 2-hour heavy rain prediction models using LSTM. The resulting output with the accuracy level of the heavy rain prediction model for 2 hours has better accuracy than the 1-hour prediction model. The 2-hour prediction model is more recommended than the 1 -hour model, with the best prediction model results being a combination of parameters 1,2,4,10, and 11 with an accuracy rate of 87%. Further modeling using 3-hour modeling on parameter combinations 1,2,4,10, and 11 resulted in an accuracy of 88% on the new test data. Modeling for a long time provides promising, accurate results but can provide significant noise. © 2024 IEEE.

关键词： Rain

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Streaming and visualising Neuronal Signals for Understanding Pain

Streaming and Visualising Neuronal Signals for Understanding...

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19th International conference on Service-Oriented Computing (ICSOC)

作者： Espinosa-Oviedo, Javier Alfonso Univ Lumiere Lyon 2 ERIC Lyon France

ISBN: (纸本)9783031141355;9783031141348

This paper presents our stream processing, and visualization system adapted to the requirements of the neuroscience domain. We propose to build a visual stream processing system for supporting the analysis and exploration of data streams in real-time by exploiting human's natural ability for discovering patterns. Our work combines stream processing and data storage techniques with data visualization theory. We study strategies for visualizing different types of data considering constraints related to real-time and data volume.

关键词： data visualization

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Sentiment analysis and Machine Learning Algorithm Implementation on Flipkart Product Customer Reviews 3

Sentiment Analysis and Machine Learning Algorithm Implementa...

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3rd International conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Nancy Deborah, R. Alwyn Rajiv, S. vinora, A. Gobinath, A. vigneswaran, S. Shiivadhanush, K.v. Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India Kamaraj College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Viruthunagar India

ISBN: (纸本)9798350348347

Sentiment analysis is a vital aspect of data mining that aims to prize private information from textual data. With the adding fashionability of online shopping platforms like Flipkart, assaying client feedback through product reviews have become a pivotal task for businesses to ameliorate their products and services. In recent times, researchers have explored the operation of machine knowledge algorithms for sentiment analysis of Flipkart product reviews. The results of this approach have demonstrated great promisein directly classifying the opposition of reviews as positive, negative, or neutral. colorful ways similar as Naive Bayes, SvM, Random Forest, Decision Trees, deep literacy, and rule-grounded approaches have been used for sentiment analysis on Flipkart reviews. also, preprocessing ways like Bag of Words, TF-IDF, Word Embeddings, and N-grams have been employed for point birth. This literature check highlights ten exploration papers published from 2017 to 2022, which bandy the perpetration of sentiment analysis and machine literacy algorithms on Flipkart product reviews. The findings of these studies give perceptivity into the effectiveness of different approaches and ways for sentiment analysis on Flipkart product reviews, and can guide unborn exploration in this field. We proposed a model called 'GRUUSE' (Gated Recurrent Unit - Universal Sentence Encoder), which can directly classify the product reviews into any one of the three orders positive, negative or neutral. It is a neural network model that converts variable-length words into numerical representations of a fixed length using GRU cells. Tasks like phrase similarity or grouping are made possible by these representations, which capture semantic meaning. © 2023 IEEE.

关键词： Sentiment analysis

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FAME: 3D Shape Generation via Functionality-Aware Model Evolution

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2022年第4期28卷 1758-1772页

作者： Guan, Yanran Liu, Han Liu, Kun Yin, Kangxue Hu, Ruizhen van Kaick, Oliver Zhang, Yan Yumer, Ersin Carr, Nathan Mech, Radomir Zhang, Hao Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada Elect Arts Ashburn VA 20147 USA Nanjing Univ Dept Comp Sci & Technol Nanjing 210093 Peoples R China NVIDIA Toronto ON M5V 3M4 Canada Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Uber ATG Pittsburgh PA 15201 USA Adobe San Jose CA 95110 USA Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D objects for data augmentation, constrained modeling, as well as open-ended exploration to possibly inspire new designs. Starting with an initial population of 3D objects belonging to one or more functional categories, we evolve the shapes through part recombination to produce generations of hybrids or crossbreeds between parents from the heterogeneous shape collection. Evolutionary selection of offsprings is guided both by a functional plausibility score derived from functionality analysis of shapes in the initial population and user preference, as in a design gallery. Since cross-category hybridization may result in offsprings not belonging to any of the known functional categories, we develop a means for functionality partial matching to evaluate functional plausibility on partial shapes. We show a variety of plausible hybrid shapes generated by our functionality-aware model evolution, which can complement existing datasets as training data and boost the performance of contemporary data-driven segmentation schemes, especially in challenging cases. Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels. At the same time, unexpected yet functional object prototypes can emerge during open-ended exploration owing to structure breaking when evolving a heterogeneous collection.

关键词： Shape Three-dimensional displays Tools Solid modeling Computational modeling Sociology Statistics Cross-category hybrids functionality-aware shape modeling functionality partial matching set evolution

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Feature Fusing with vortex-Based Classification of Sentiment analysis Using Multimodal data 8th

Feature Fusing with Vortex-Based Classification of Sentiment...

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8th International conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Sunil Kumar, v. Renukadevi, S. Yashaswini, B.M. Malagi, vindhya P. Pareek, Piyush Kumar Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Dayananda Sagar College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9789819976218

For the purpose of identifying the felt emotions and intentions behind multimodal data, multimodal sentiment investigation seeks to semantic info acquired across different modalities. The primary focus of this field of study is the design of a novel fusion system that can efficiently and effectively aggregate data from several sources. However, the ability to use the independence and connection across modalities is lacking in prior work, preventing optimal performance. Consequently, the work suggests a unique approach to visual and textual sense modality and then fuses these features using different kernel learning techniques (MKL). After that, we feed the combined dataset into an Extreme Learning Machine (ELM), where we use the vortex Search Algorithm to choose the most appropriate bias and weight (vSA). When everything is said and done, trials are run on two publicly accessible datasets, and the results are better than the current gold standard for multimodal sentiment analysis. For instance, whereas the current ML only manages 94% accuracy, the suggested ELM-vSA achieves 98.50%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extreme learning machine Feature fusion Multimodal sentiment analysis Multiple kernel learning vortex search algorithm

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Using Business Intelligence to Analyze Road Traffic Accidents 22

Using Business Intelligence to Analyze Road Traffic Accident...

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conference on Central and Eastern European eDem / eGov Days on Hate Speech and Fake News - Fate or Issue to Tackle(CEEE Gov Days)

作者： Awad, Mohammed Al Redhaei, Aneesa Fraihat, Salam Amer Univ Ras Al Khaimah Rak U Arab Emirates Ajman Univ AIRC Coll Engn & Informat Technol Ajman U Arab Emirates

ISBN: (纸本)9781450397667

Road Traffic Accidents constitute a significant concern around the world. Understanding the primary and contributing factors may combat traffic accidents and mitigate their impact. Based on an actual traffic accidents dataset of the United Arab Emirates (UAE) between 2012-2019, we investigate the importance of data science and Business Intelligence (BI) in visualizing traffic accidents in a descriptive format. The proposed BI solution provides visual data exploration for authorities to analyze and make informed decisions. This paper provides an example of how open data can save lives and resources. The design and implementation of the BI solution and its features are also presented in this paper.

关键词： Business intelligence data analytics Road traffic accidents Traffic management E-government Descriptive analysis

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Integrated Approach for the Characterization of Low Resistivity and Low Contrast Pays - A Case Study from Mahanadi Offshore, India

Integrated Approach for the Characterization of Low Resistiv...

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2024 SPE Gas and Oil Technology conference, GOTECH 2024

作者： Zhunussova, Gulzira Z. Panchakarla, Anjana Jha, Bhawesh vardhan, Harsh Rajesekar, v. Baker Hughes India ONGC India India

ISBN: (纸本)9781959025405

The deep water formations from Mahanadi Offshore is one of the major focus for hydrocarbon exploration in India. The reservoirs in this field primarily have low resistivity and low contrast character due to presence of thinly laminated shale-sand laminae with overlying shales. Conventional logs do not sufficiently distinguish between the overlying shales against the laminated reservoir intervals- owing to poor vertical resolution of the tools to characterize the thin beds properties. The operating company followed the standard acquisition of conventional logging data along with tensor resistivity measurement for a laminated-shaly sand analysis (LSSA) followed by formation testing. While multiple new pay sands were discovered through this method, the sand-shale-fluid model of LSSA is insufficient to provide fluid typing. Furthermore, fluid sampling using formation testers in these formations are risky due to unconsolidated nature of the formation, Plugging & SOBM invasion etc. Thus, the delineation of the formation fluid using methods other than just the formation testing is critical. The acquisition of magnetic resonance data and an integrated interpretation involving conventional logging, tensor resistivity and NMR data helped overcome these challenges, prior to formation testing. The electrical anisotropy and borehole image data helped identify thin beds. LSSA outputs based on conventional and tensor resistivity data indicated hydrocarbon bearing intervals. A continuous magnetic resonance measurement acquiring the longitudinal relaxation (T1), transverse relaxation (T2) and the diffusion coefficient (D), helped derive a lithology independent fluid typing in these wells to provide insights before formation testing. Hydrocarbon bearing sands were identified as per the results of the integrated analysis, and the formation testing confirmed the same. Conventional resistivity inversion, LSSA saturation and flushed zone saturation from magnetic resonance provided an und

关键词： Sand

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A Method for Identifying Use Cases in data-Driven Product Management 31

A Method for Identifying Use Cases in Data-Driven Product Ma...

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31st CIRP conference on Life Cycle Engineering, LCE 2024

作者： Fichtler, Timm Kirchberg, Lisa Grigoryan, Khoren Koldewey, Christian Dumitrescu, Roman Heinz Nixdorf Institute University of Paderborn Fürstenallee 11 Paderborn33102 Germany Fraunhofer Institute for Mechatronic Systems Design Zukunftsmeile 1 Paderborn33102 Germany

In today's rapidly changing business environment, characterized by shifting customer needs, volatile markets, and uncertain competition, product management must gather and synthesize diverse requirements to make informed product decisions and effectively position products in the market. Increasing digitization generates product-related data from diverse sources, such as usage and social media information. Leveraging and analyzing this data has the potential to improve the quality of product decisions. However, the range of data-driven opportunities in product management is vast and varied, requiring companies to identify appropriate starting points for implementation. This paper presents a method for identifying and selecting potential use cases for data-driven product management developed using action design research in five industrial companies. The method includes support for 1) recording the initial situation and tasks of the product management in the given company, 2) the identification of opportunities for beneficial data analysis, 3) the derivation and documentation of potential use cases, and 4) the prioritization of the use cases to derive a possible implementation roadmap. Hence, the methodology provides significant support for an initial exploration of use cases in data-driven product management through a tested approach. © 2024 Elsevier B.v.. All rights reserved.

关键词： data Analytics

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Robot/UAv Indoor visual SLAM in Smart Cities Based on Remote data Processing

Robot/UAV Indoor Visual SLAM in Smart Cities Based on Remote...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Jharko, Elena Mamchenko, Mark Khripunov, Sergey P. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Laboratory of Distributed Information and Control Systems Moscow Russia V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Laboratory of Cyber-Physical Systems Moscow Russia

ISBN: (纸本)9781665464291

Indoor navigation is a topical issue in the use of robotics and unmanned aerial vehicles (UAvs) in a smart city, especially in the absence/weak signals of satellite navigation. One of the promising solutions of these issues is the use of visual simultaneous localization and mapping (vSLAM) algorithms, and one or more remote data processing servers for localization and map construction of the robots and the UAvs. The paper analyzes the current state of remote vSLAM and gives the requirements of a promising similar framework for UAvs in a smart city. In general, it is possible to distinguish two approaches to remote vSLAM systems for robots/UAvs. The first one implies sending raw data from sensors (video stream) via wireless connection directly to a remote server, which can be used for cloud/fog/edge computing. The second approach involves data pre-processing on the robots/UAvs, with subsequent transmission of data to the remote cloud/fog/edge computing server for further processing, acquiring map keyframes and map updates. The considered approaches, algorithms and solutions are classified by the type and the number of servers used, the maximum number of robotic agents/UAvs, the presence of navigation data fusion, as well as the type of video sensor used. Based on the analysis of existing solutions and vSLAM classification, it is possible to form a set of requirements for promising vSLAM systems for indoor navigation of robots/UAvs, including their camera input, resolution, number of frames per second (FPS), bandwidth, support of wireless standards, channels, and protocols, multi-robot/multi-server support, and time consumption. © 2023 IEEE.

关键词： Robots

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