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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

共 2237 条记录，以下是401-410 订阅

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Unsupervised Detection of Solving Strategies for Competitive Programming 22nd

Unsupervised Detection of Solving Strategies for Competitive...

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22nd international conference on intelligent data engineering and automated learning

作者： Stoica, Alexandru Stefan Balbiceanu, Daniel Mihaescu, Marian Cristian Rebedea, Traian Univ Craiova Craiova Romania Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9783030916077;9783030916084

Transformers are becoming more and more used for solving various Natural Language Processing tasks. Recently, they have also been employed to process source code to analyze very large code-bases automatically. This paper presents a custom-designed data analysis pipeline that can classify source code from competitive programming solutions. Our experiments show that the proposed models accurately determine the number of distinct solutions for a programming challenge task, even in an unsupervised setting. Together with our model, we also introduce a new dataset called AlgoSol-10 for this task that consists of ten programming problems together with all the source code submissions manually clustered by experts based on the algorithmic solution used to solve each problem. Taking into account the success of the approach on small source codes, we discuss the potential of further using transformers for the analysis of large code bases.

关键词： Transformers Competitive programming Source code analysis Unsupervised learning

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Feature Extraction and intelligent Generation of Spatial Environment Design Combined with Deep learning 2

Feature Extraction and Intelligent Generation of Spatial Env...

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2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Dong, Chuifeng Guangdong Baiyun University Guangzhou China

ISBN: (纸本)9798350373646

In this paper, MATLAB graphical user interface is used to complete the virtual monitoring and analysis experiment in the living environment. Through MATLAB Web server, a set of space environment virtual monitoring and analysis experiment system based on MATLAB and Web is formed. A feature extraction algorithm of saccade signal based on common spatial pattern (CSP) is proposed. This filter is used to filter the multi-guide eye movement signal in the spatial domain, and it is used as the characteristic parameter of the scanning signal. Through the test platform, users can easily browse and monitor the parameters of the living environment, and perform the corresponding physical property parameters calculation and prediction as needed. This is convenient and fast, and greatly improves the efficiency of monitoring and analysis of the living environment. At the same time, the required space environment parameters or physical property data can be displayed intuitively on the interface, which is simple and fast, and can greatly improve the efficiency of monitoring and analysis. © 2024 IEEE.

关键词： Eye movements

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Application of data Fusion Based on Clustering-Neural Network for ETC Gantry Flow Capacity Correction 22

Application of Data Fusion Based on Clustering-Neural Networ...

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22nd COTA international conference of Transportation Professionals (CICTP) - Intelligence, Connectivity, and Mobility

作者： Zhao, Yan Zhang, Bing Cao, Yang Rui, Yikang Ran, Bin Southeast Univ Sch Transportat Nanjing Peoples R China Jiangsu Yanjiang Expressway Co Ltd Changshu Peoples R China

ISBN: (数字)9780784484265

ISBN: (纸本)9780784484265

Electronic Toll Collection (ETC) gantries are beneficial on highways and feature non-stop charging, improve highway traffic efficiency, and alleviate congestion. ETC gantry data is highly accurate, however, abnormal data detection can occur in some adverse driving environments. To improve the accuracy of flow data, a multi-source data fusion model based on K-means RBF neural network is proposed. A multi-source data fusion is carried out using traffic survey and ETC gantry data. The fusion result is compared with the detection result of the ETC gantry and other fusion methods. Results show the model has adaptive learning characteristics and realizes the high-precision correction of the running state of traffic flow in abnormal environments by learning the historical law of multi-source data which overcomes the problem of data detection of a single detector due to a bad driving environment and demonstrates the potential of multi-source data fusion in the transportation field.

关键词： data fusion

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2nd international Semantic Intelligence conference, ISIC 2022

2nd International Semantic Intelligence Conference, ISIC 202...

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2nd international Semantic Intelligence conference, ISIC 2022

ISBN: (纸本)9789811971259

The proceedings contain 22 papers. The special focus in this conference is on Semantic Intelligence. The topics include: Scrutinize and Discover of Image of Freshwater Taken by Faraway Realizing Using FFNN and ConvNet Mechanisms;knowledge-based Extraction of Cause–Effect Relations from Biomedical Text;nyOn: A Multilingual Modular Legal Ontology for Representing Court Judgements;Technologies for AI-Driven Fashion Social Networking Service with E-Commerce;deep learning-Based Classification of Customer Communications of a German Utility Company;Short Analysis of the Impact of COVID-19 Ontologies;demystifying Semantic Intelligence for Enabling intelligent Applications;Sentiment Analysis of Public Health Concerns of Tokyo 2020 Olympics Using LSTM;web-Based Visualization and Analysis Framework for Graph data;internet of Nano and Bio-Nano Things: A Review;iTelos—Case Studies in Building Domain-Specific Knowledge Graphs;comparative Study of Image Encryption and Image Steganography Using Cryptographic Algorithms and Image Evaluation Metrics;web Service Credibility Evaluation Methods in Different Application Domains;semantic Web Ontology for Botnet Classification;design and Performance Evaluation of a Multi-patient Health Monitoring System;discovering Novelty via Transfer learning;an Ontology for Social Media data Analysis;the Coronavirus Disease Ontology (CovidO);an Ethnolinguistic Research Agenda for intelligent Autonomous Systems.

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Attention-Augmented Parametric Kernel Graph Neural Network (APKGNN) for Node Classification 22

Attention-Augmented Parametric Kernel Graph Neural Network (...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Bose, Avishek Hsu, William H. Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States Kansas State University Department of Computer Science and Engineering ManhattanKS United States

ISBN: (纸本)9798350345346

We present a new graph neural network, the Attention-based Parametric-Kernel augmented Graph Neural Network (APKGNN), developed for node classification tasks. Despite extensive work on modeling multi-faceted relationships between connected nodes of a graph, the effect of attention on edge features mapped to relationships has not yet been analyzed through learning representation. This study derives such an attention vector by first calculating node features corresponding to endpoints of an edge and then aggregating these with extracted local intrinsic patches of a given graph to generate augmented local patch vectors. This process uses a parametric kernel based on Gaussian mixture models (GMMs) to embed local neighborhoods of the graph in local patches. The patch vectors then convolve with the above node features to produce an updated node representation. We show that this new learning representation (APKGNN) achieves higher node classification accuracy on tasks - both standard benchmarks (Cora, PubMed, Citeseer) and new experimental short text corpora where nodes correspond to text documents and words. This implementation of the GNN convolution layer outperforms state-of-the-art (SOTA) algorithms, achieving higher training, validation, and test accuracy by a significant margin on three standard benchmark data sets under both SOTA experimental settings and those for new testbeds. © 2023 IEEE.

关键词： APKGNN attention vector local patch multi-processing neighborhood sampling parametric kernel

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A Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks 19th

A Cooperative Feature Removal Mechanism for Cell Outage Det...

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19th international conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Wrona, Andrea Gentile, Simone De Santis, Emanuele Giuseppi, Alessandro Pietrabissa, Antonio Delli Priscoli, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Lazio Rome00185 Italy Department of Electrical and Information Engineering Polytechnic of Bari via Re David 200 Puglia Bari70125 Italy

ISBN: (纸本)9783031842597

In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data aggregation at the core network, which is responsible for identifying cell failures. Proposing a novel approach, we leverage a Machine learning-based distributed and cooperative feature removal mechanism in order to preserve the privacy of data and avoid any degradation in classification performance. Simulations carried out on a dataset retrieved from a real 4G-LTE rollout demonstrate that the proposed approach, through a cooperation among agents, maintains or even slightly improves accuracy, precision, recall, and F1-score in outage prediction compared to other conventional methods, showcasing its efficacy for cell outage detection purposes while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A Survey on Detection of Various Casting Defects Using Deep learning Techniques

A Survey on Detection of Various Casting Defects Using Deep ...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： M. Maheswari N. C. Brintha Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Anandnagar

In the mass production process, producing the product quality is a challenging task, because of the metal casting process, the presence of the product varies irregularly due to the kinds of defects. Identification of faulty products early in an automatic manner is one of the challenges in the industry. This work is a systematic review of various kinds of defects in the casting process, automated defect detection systems with deep learning approaches, and an analysis of their performance. Deep learning approaches are used to produce high-quality products in production lines and enhance the quality inspection process earlier in an automatic manner.

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learning through play: Design and creation of a narrative Game-Based learning experience 22

Learning through play: Design and creation of a narrative Ga...

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22nd Brazilian Symposium on Human Factorsin Computing Systems, IHC 2023

作者： Vieira, Bruno Rodrigues Santos, Caroline Queiroz Diamantina Brazil

ISBN: (纸本)9798400717154

Digital didactic games are promising tools to aid the teaching-learning process, as they bring the previously abstract content closer to the student's reality. This study aimed to develop a narrative digital game to support the development of skills and knowledge of specific topics in engineering students. For this, it was necessary to study and understand the design and development process of an educational digital game, evaluating the playful and pedagogical elements that could be used. The work methodology was structured in three stages: 1) game design, 2) application of the game to the research participants, and;3) data collection and analysis. The main perceptions of the participants about the game, the obstacles and facilitators during the gameplay, and the possible contributions in relation to the design and development of the game were analyzed. The results showed that game-based learning is promising and innovative, as it encourages students' interest and motivation about the themes through resources beyond the classroom space. © 2023 ACM.

关键词： Game design

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An Investigation of Large Language Models for Real-World Hate Speech Detection 22

An Investigation of Large Language Models for Real-World Hat...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Guo, Keyan Hu, Alexander Mu, Jaden Shi, Ziheng Zhao, Ziming Vishwamitra, Nishant Hu, Hongxin University at Buffalo United States Union County Magnet High School United States East Chapel Hill High School United States Minhang Corsspoint Academy University of Texas at San Antonio United States

ISBN: (纸本)9798350345346

Hate speech has emerged as a major problem plaguing our social spaces today. While there have been significant efforts to address this problem, existing methods are still significantly limited in effectively detecting hate speech online. A major limitation of existing methods is that hate speech detection is a highly contextual problem, and these methods cannot fully capture the context of hate speech to make accurate predictions. Recently, large language models (LLMs) have demonstrated state-of-the-art performance in several natural language tasks. LLMs have undergone extensive training using vast amounts of natural language data, enabling them to grasp intricate contextual details. Hence, they could be used as knowledge bases for context-aware hate speech detection. However, a fundamental problem with using LLMs to detect hate speech is that there are no studies on effectively prompting LLMs for context-aware hate speech detection. In this study, we conduct a large-scale study of hate speech detection, employing five established hate speech datasets. We discover that LLMs not only match but often surpass the performance of current benchmark machine learning models in identifying hate speech. By proposing four diverse prompting strategies that optimize the use of LLMs in detecting hate speech. Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech by fully utilizing the knowledge base in LLMs, significantly outperforming existing techniques. Furthermore, although LLMs can provide a rich knowledge base for the contextual detection of hate speech, suitable prompting strategies play a crucial role in effectively leveraging this knowledge base for efficient detection. © 2023 IEEE.

关键词： few-shot learning hate speech large language model prompt engineering

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Unknown Type Streaming Feature Selection via Maximal Information Coefficient 22

Unknown Type Streaming Feature Selection via Maximal Informa...

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22nd IEEE international conference on data Mining (ICDM)

作者： Zhou, Peng Zhang, Yunyun Yan, Yuanting Zhao, Shu Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ China Hefei Peoples R China

ISBN: (纸本)9798350346091

Feature selection aims to select an optimal minimal feature subset from the original datasets and has become an indispensable preprocessing component before data mining and machine learning, especially in the era of big data. Most feature selection methods implicitly assume that we can know the feature type (categorical, numerical, or mixed) before learning, then design corresponding measurements to calculate the correlation between features. However, in practical applications, features may be generated dynamically and arrive one by one over time, which we call streaming features. Most existing streaming feature selection methods assume that all dynamically generated features are the same type or assume we can know the feature type for each new arriving feature on the fly, but this is unreasonable and unrealistic. Therefore, this paper firstly studies a practical issue of Unknown Type Streaming Feature Selection and proposes a new method to handle it, named UT-SFS. Extensive experimental results indicate the effectiveness of our new method. UT-SFS is nonparametric and does not need to know the feature type before learning, which aligns with practical application needs.

关键词： feature selection streaming feature unknown feature type maximal information coefficient

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