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检索条件"任意字段=International Conference on Computational Methods and Data Engineering, ICMDE 2020"

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international conference on computational methods and data engineering, icmde 2020

International Conference on Computational Methods and Data E...

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international conference on computational methods and data engineering, icmde 2020

ISBN: (纸本)9789811579066

The proceedings contain 50 papers. The special focus in this conference is on computational methods and data engineering. The topics include: Energy-efficient schemes in underwater wireless sensor network: A review;information hiding techniques for cryptography and steganography;geospatial knowledge management-fresh fuel for banking and economic growth?;affect recognition using brain signals: A survey;"memorize, reproduce, and forget" inclination;students’ perspectives: A study of selected universities in ghana;neural network and pixel position shuffling-based digital image watermarking;hybrid optimized image steganography with cryptography;txtlineseg: Text line segmentation of unconstrained printed text in devanagari script;intelligent strategies for cloud computing risk management and testing;effective survey on handwriting character recognition;preface;Software quality optimization of coupling and cohesion metric for CBSD model;enhancement in braille systems—a survey;a critical review on use of data mining technique for prediction of road accidents;rio olympics 2016 on twitter: A descriptive analysis;a survey on vehicle to vehicle communication;obd-ii and big data: A powerful combination to solve the issues of automobile care;obu (on-board unit) wireless devices in vanet(s) for effective communication—a review;chinese postman problem: A petri net based approach;weather dataset analysis using apache pig;analysis of learner’s behavior using latent dirichlet allocation in online learning environment;an overview of recent developments in convolutional neural network (cnn) based face detector;detecting wsn attacks through hmac and sch formation;a review of artificial intelligence techniques for requirement engineering;data-driven model for state of health estimation of lithium-ion battery;smell-o-vision device.

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international conference on computational methods and data engineering, icmde 2020

International Conference on Computational Methods and Data E...

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international conference on computational methods and data engineering, icmde 2020

ISBN: (纸本)9789811568756

The proceedings contain 50 papers. The special focus in this conference is on computational methods and data engineering. The topics include: On roman domination of graphs using a genetic algorithm;general variable neighborhood search for the minimum stretch spanning tree problem;tabu-embedded simulated annealing algorithm for profile minimization problem;deep learning-based asset prognostics;evaluation of two feature extraction techniques for age-invariant face recognition;XGBoost: 2D-object recognition using shape descriptors and extreme gradient boosting classifier;Comparison of principle component analysis and stacked autoencoder on NSL-KDD dataset;maintainability configuration for component-based systems using fuzzy approach;development of petri net-based design model for energy efficiency in wireless sensor networks;Hybrid ANFIS-GA and ANFIS-PSO based models for prediction of type 2 diabetes mellitus;lifting wavelet and discrete cosine transform-based super-resolution for satellite image fusion;biologically inspired intelligent machine and its correlation to free will;weather status prediction of Dhaka City using machine learning;image processing: What, how and future;a study of efficient methods for selecting quasi-identifier for privacy-preserving data mining;day-ahead wind power forecasting using machine learning algorithms;query relational databases in Punjabi language;machine learning algorithms for big data analytics;fault classification using support vectors for unmanned helicopters;EEG signal analysis and emotion classification using bispectrum;social network analysis of youtube: A case study on content diversity and genre recommendation;Slack feedback analyzer (SFbA);a review of tools and techniques for preprocessing of textual data;A U-shaped printed UWB antenna with three band rejection;model for predicting academic performance through artificial intelligence.

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A survey of data-driven insights into air quality trends and prediction in India 4th

A survey of data-driven insights into air quality trends and...

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4th international conference on computational methods in Science and Technology, ICCMST 2024

作者： Kabilan, K. Jebathangam, J. Department of Computer Science VISTAS Chennai India

ISBN: (纸本)9781032911571

Air pollution is a major health and environmental issue in increasingly urbanizing countries like India. This study examines ML and DL implementations on Indian Air Quality Index (AQI) datasets from 2015 to 2020. The research seeks to identify patterns, anticipate trends, and guide air pollution mitigation decisions. The study carefully analyses AQI parameters, explores data, and uses powerful ML and DL algorithms to forecast AQI and identify pollution hotspots. Technical features include missing value imputation, feature engineering, model selection criteria, hyper parameter tuning procedures, and model performance assessment measures. Key results show that ML and DL models can accurately forecast air quality, identify pollution sources, and capture temporal and geographical fluctuations. The article suggests integrating more data sources, exploring advanced deep learning methods, and developing dynamic air quality control systems. This study uses data-driven insights and multidisciplinary techniques to improve our knowledge of air quality dynamics in India and create the framework for proactive air pollution mitigation and public health policies. © 2025 the Author(s).

关键词： data quality

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Exploring multi-fidelity noisy data: methods and real-world examples 31

Exploring multi-fidelity noisy data: methods and real-world ...

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31st international conference on Noise and Vibration engineering, ISMA 2024 and 10th international conference on Uncertainty in Structural Dynamics, USD 2024

作者： Giannoukou, K. Ascia, P. Marelli, S. Duddeck, F. Sudret, B. ETH Zurich Department of Civil Environmental and Geomatic Engineering Stefano-Franscini-Platz 5 Zurich8093 Switzerland Technical University of Munich TUM School of Engineering and Design Theresienstraße 90 Munich80333 Germany

ISBN: (纸本)9789082893175

Multi-fidelity surrogate models (MFSMs) are a well-established tool to combine information from sources with diverse computational fidelities into a single surrogate model. The sources of higher or lower fidelity can be, for example, computer simulations or physical experiments. MFSMs can exhibit enhanced predictive accuracy and reduced costs in emulating the response of complex systems, outperforming their single-fidelity surrogate model counterparts at comparable training costs. In real-world applications, uncertainty is present in the data, regardless of their fidelity. This uncertainty can be due to measurement noise, numerical noise, or unobserved/latent variables, and adds a layer of complexity by introducing non-deterministic behavior in the system response. In this work, we provide a framework to address the uncertainty in MFSM scenarios. The effectiveness of our approach is demonstrated through a transfer learning application in crashworthiness and a real-world wind turbine application, showcasing the applicability and versatility of our proposed methods. © 2024 Proceedings of ISMA 2024 - international conference on Noise and Vibration engineering and USD 2024 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Wind turbines

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AuRoRA: A One-for-all Platform for Augmented Reasoning and Refining with Task-Adaptive Chain-of-Thought Prompting 30

AuRoRA: A One-for-all Platform for Augmented Reasoning and R...

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Joint 30th international conference on computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zou, Anni Zhang, Zhuosheng Zhao, Hai Li, Xiaoshan Bian, Minjie Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Technology Innovation Department Shanghai Data Group Co. Ltd. China Shanghai Data Group Co. Ltd. China

ISBN: (纸本)9782493814104

Large language models (LLMs) empowered by chain-of-thought (CoT) prompting have yielded remarkable prowess in reasoning tasks. Nevertheless, current methods predominantly lean on handcrafted or task-specific demonstrations, lack reliable knowledge basis and thus struggle for trustworthy responses in an automated pattern. While recent works endeavor to improve upon one certain aspect, they ignore the importance and necessity of establishing an integrated and interpretable reasoning system. To address these drawbacks and provide a universal solution, we propose AuRoRA: a one-for-all platform for augmented reasoning and refining based on CoT prompting that excels in adaptability, reliability, integrity, and interpretability. The system exhibits superior performances across six reasoning tasks and offers real-time visual analysis, which has pivotal academic and application value in the era of LLMs. The AuRoRA platform is available at https://***/spaces/Anni123/AuRoRA. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Refining

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Rethinking-based Code Summarization with Chain of Comments 31

Rethinking-based Code Summarization with Chain of Comments

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31st international conference on computational Linguistics, COLING 2025

作者： Cao, Liuwen He, Hongkui Huang, Hailin Wang, Jiexin Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9798891761964

Automatic code summarization aims to generate concise natural language descriptions (summary) for source code, which can free software developers from the heavy burden of manual commenting and software maintenance. Existing methods focus on learning a direct mapping from pure code to summaries, overlooking the significant heterogeneity gap between code and summary. Moreover, existing methods lack a human-like re-check process to evaluate whether the generated summaries match well with the code. To address these two limitations, we introduce RBCoSum, a novel framework that incorporates the generated Chain Of Comments (COC) as auxiliary intermediate information for the model to bridge the gap between code and summaries. Also, we propose a rethinking process where a learned ranker trained on our constructed ranking dataset scores the extent of matching between the generated summary and the code, selecting the highest-scoring summary to achieve a re-check process. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models as well as multiple code Large Language Models (LLMs). The experimental results show that RBCoSum is effective and outperforms baselines by a large margin. The human evaluation also proves the summaries generated with RBCoSum are more natural, informative, useful, and truthful. © 2025 Association for computational Linguistics.

关键词： computational linguistics

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Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting 6

Explainable Deep Learning with Embedded Feature Selection fo...

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2024 international conference on Smart Systems and Technologies

作者： Jimenez-Navarro, M. J. Troncoso-Garcia, A. R. Troncoso, A. Martinez-Alvarez, F. Martinez-Ballesteros, M. Univ Seville Dept Comp Languages & Syst Seville Spain Pablo de Olavide Univ Data Sci & Big Data Lab Seville Spain

ISBN: (纸本)9798350386394;9798350386400

Electricity demand forecasting is an important part of the energy industry strategy. Accurate predictions are crucial for maintaining a stable energy supply, planning production, managing distribution, preventing grid overloads, integrating renewable energy sources, and reducing costs and environmental impact. Machine learning and, in particular, deep learning are promising techniques to improve the prediction accuracy of electric demand, but face challenges related to a lack of interpretability due to the "black box" nature of some models. Feature selection methods address these issues by identifying relevant features and simplifying the learning process, This paper aims to explain the most critical lags that impact electric demand forecasting in Spain using the temporal selection layer technique within deep learning models for time series forecasting. This technique transforms a neural network into a model with embedded feature selection, aiming to enhance efficacy and interpretability while reducing computational costs. The results were compared with other methods that incorporate an embedded feature selection mechanism to select the best model. Furthermore, an explainable technique is used to assess the feature importance in the best model over the last year to understand how input features influence electric demand forecasting and provide insights into their contributions and interactions. The results show that our approach improves both the efficacy and interpretability in the context of electric demand forecasting.

关键词： Deep learning Electric demand Feature selection Time series forecasting XAI

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RIGID PROTEIN-PROTEIN DOCKING VIA EQUIVARIANT ELLIPTIC-PARABOLOID INTERFACE PREDICTION 12

RIGID PROTEIN-PROTEIN DOCKING VIA EQUIVARIANT ELLIPTIC-PARAB...

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12th international conference on Learning Representations, ICLR 2024

作者： Yu, Ziyang Huang, Wenbing Liu, Yang Department of Computer Science Tsinghua University China Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

The study of rigid protein-protein docking plays an essential role in a variety of tasks such as drug design and protein engineering. Recently, several learning-based methods have been proposed, exhibiting much faster docking speed than those computational methods. In this paper, we propose a novel learning-based method called ElliDock, which predicts an elliptic paraboloid to represent the protein-protein docking interface. To be specific, our model estimates elliptic paraboloid interfaces for the two input proteins respectively, and obtains the roto-translation transformation for docking by making two interfaces coincide. By its design, ElliDock is independently equivariant with respect to arbitrary rotations/translations of the proteins, which is an indispensable property to ensure the generalization of the docking process. Experimental evaluations show that ElliDock achieves the fastest inference time among all compared methods and is strongly competitive with current state-of-the-art learning-based models such as DiffDock-PP and Multimer particularly for antibody-antigen docking. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Proteins

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Enhancing Sales Forecasting Accuracy in the Presence of Missing data 17

Enhancing Sales Forecasting Accuracy in the Presence of Miss...

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IEEE Congress on Cybermatics: 17th IEEE international conference on Internet of Things, iThings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart data, Smartdata 2024

作者： Tang, Jun Sun, Jingpeng Guo, Bing Shen, Yan Dai, Shengxin Wang, Peng College of Computer Science Sichuan University Chengdu China College of Computer Science University of Birmingham Dubai United Arab Emirates School of Computer Science Chengdu University of Information Technology Chengdu China College of Software Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350351637

Accurate sales forecasting serves as a critical foundation for informed business decision-making and numerous methodologies have been advanced by scholars predominantly rely on complete datasets, however, in practice, uncontrollable variables often result in the presence of missing data, diminishing the applicability of established techniques, so the sales forecasting research based on missing data is of great significance. Some researchers have resorted to employing deletion or interpolation techniques to pre-process incomplete data before conducting sales forecasts, but such completion methods tend to introduce distributional biases, subsequently leading to forecast inaccuracies. In light of these challenges, this study introduces a novel computational framework combining Convolutional Neural Network (CNN), Self-Attention, and Sequence-to-Sequence (Seq2Seq) models, where global features are first extracted via the synergistic application of CNN and Self-Attention mechanisms, then the extracted features are seamlessly integrated with the original dataset, supplanting conventional interpolation strategies, and finally the amalgamated data are inputted into the Seq2Seq model for sales prediction. The proposed methodology is empirically evaluated using sales data from a real-world company, demonstrating commendable predictive performance and underscoring the efficacy of the proposed approach in enabling accurate sales forecasts within the context of missing data scenarios. © 2024 IEEE.

关键词： data accuracy

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Multi-Dimensional Business data Fusion Modeling Based on Dynamic Bayesian Network 29th

Multi-Dimensional Business Data Fusion Modeling Based on Dyn...

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29th international conference on computational and Experimental engineering and Sciences, ICCES 2023

作者： Luo, Tianshu Lu, Dan Han, Qilong Sui, Shanshan College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (纸本)9783031425141

Business data fusion from various data sources in enterprises has been one of the hot issues in current research. While previous work has made great efforts to achieve data fusion, existing methods still suffer from three key limitations: (i) Most methods do not guarantee accurate connections to the data sources;(ii) Most approaches disregard the time-varying of business data;(iii) These approaches ignore the fact that the dimensionality of business data is high, resulting in query instances failing. To solve the above problems, we propose a novel multi-dimensional business data fusion method based on Dynamic Bayesian Networks, namely DBN. Specifically, we design a DBN for all the attributes in the data and generate a random variable for each attribute, which can be learned based on domain dependence and correlation or traditional structure. Then, we compute the query using the underlying joint distribution of the Bayesian Network. Moreover, we use Structured Query Language (SQL) engine in an Electric Motor Temperature multi-sensor business data source for query and analysis. The experimental results show that our method can achieve state-of-the-art performance and exhibit competitive accuracy of data source connections. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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