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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

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Automatic Identification of Chinese Paired Discourse Connectives 17

Automatic Identification of Chinese Paired Discourse Connect...

引用

17th IEEE international conference on Semantic Computing (ICSC)

作者： Costa, Nelson Filipe Cheng, Yushun Muermans, Thomas Chapados Hanel, Blaise Kosseim, Leila Concordia Univ Computat Linguist Concordia CLaC Lab Dept Comp Sci & Software Engn Montreal PQ Canada

ISBN: (纸本)9781665482639

This paper describes our approach to automatically identify paired Discourse Connectives (DCs) in Chinese texts. Discourse Connectives (DCs) are terms that connect two text spans and signal the discourse relations between them. Most DCs consist of a consecutive words (eg. as a result);however paired DCs are composed of non-consecutive words that together signal the discourse relation (eg. on one hand ... on the other hand). Although paired DCs are not common in English, they are very frequent in Chinese. The contribution of this paper in two-fold: First, we propose a methodology for the automatic identification of Chinese paired DCs. Second, we present a new corpus based on the Chinese Discourse Treebank (CDTB) [1] annotated with paired DCs. To identify paired DCs, we experimented with two main approaches: hypothesis testing and supervised machine learning. Although the hypothesis testing approaches led to lower than expected results, the simple machine learning models achieved F-scores between 72.5%-75.6% with no fine-tuning.

关键词： Chinese Discourse Treebank Corpus Creation Discourse Connectives Hypothesis Testing machine learning

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A Novel Approach of Image Caption Generator using Deep learning 3

A Novel Approach of Image Caption Generator using Deep Learn...

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3rd international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Saini, DilipKumar Jang Bahadur Kumar, Sunil Joshi, Kapil Pathak, Abhishek Kumar Jain, Saksham Singh, Anupam Pimpri Chinchwad University Department of Computer Science and Engineering Pune412106 India Meerut India Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India Upes Dehradun India Meerut India Graphic Era Hill University Department of Computer Science and Engineering dehradun India

ISBN: (纸本)9798350306989

Image caption generation is an emerging field of study for researchers that mainly focuses on developing systems that can generate captions of an image. In today's World, Image captioning is a very useful tool. Moreover, some systems use machine learning models such as deep learning models which include CNNs and RNNs models used to analyze images and generate captions. There are some late growths in caption generation that have focused on transfer learning, reinforcement learning, and multimodal approaches. The proposed system has 5 phases which are data cleaning, extraction, layering, training, and testing. The proposed model is tested on the recent Flicker8k_Dataset for image caption generation and it is implemented using Python software. © 2023 IEEE.

关键词： Long short-term memory

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machine learning Algorithms – The Effect of Training and Testing Process 5th

Machine Learning Algorithms – The Effect of Training and Te...

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5th international conference on Data Science, machine learning and Applications, ICDSMLA 2023

作者： Vasundhara, S. Tejaswini, S. Sriyam, Addepalli Sai Department of Humanities & Mathematics G. Narayanamma Institute of Technology and Science Shaikpet Hyderabad500104 India G. Narayanamma Institute of Technology and Science Shaikpet Hyderabad500104 India

ISBN: (纸本)9789819780426

Newer perspectives toward technology aim to leverage current methods to achieve even greater heights. machine learning, a game-changer in the real world, assists machines in progressively improving their performance. It also enables software applications to accurately predict outcomes without explicit programming. Traditionally, machine learning algorithms use past data for predictions. In a typical machine learning environment, the primary focus is on data training. The collected data is divided into two purposes: one for training and the other for testing. Common practice suggests reserving a higher percentage of data for training the machine because the more the machine is trained, the better its results become. While the machine learns from the training data, its capabilities are assessed based on its performance on unknown data. If the machine can produce convincing results on unknown datasets, it is likely to perform well on real-time data. As a result, data testing takes centre stage. Therefore, in this paper, the proposed idea is of using limited yet efficient datasets for training and reserving the rest of the data for testing the machine. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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FL4C^2: A Trustworthy Intelligent Cloud Computing for Private machine learning 2

FL4C^2: A Trustworthy Intelligent Cloud Computing for Privat...

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2nd international conference on Applied Statistics, Computational Mathematics, and software engineering, ASCMSE 2023

作者： Yang, He An, Jian He, Xin Shen, Yuhao School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China School of Software Henan University Kaifeng475001 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

The cloud operating system plays a crucial role in supporting machine learning tasks by offering powerful computing capabilities and abundant resources. This enables the implementation of various complex applications, including health monitoring and noise pollution assessment. However, this paradigm involves centralized data storage and processing on a central server, which raises significant data security concerns. To tackle these security issues, we propose a state-of-the-art framework called FL4C^2, which amalgamates federated learning (FL) and cloud computing (CC) eliminating their inherent drawbacks. Specifically, FL4C^2 allows users to collaboratively learn a shared model by solely submitting model parameters to the server, which ensures that user data remains localized and private, thereby preventing any potential data leakage. Moreover, FL4C^2 incorporates an encryption parameter transmission mechanism, which safeguards against indirect data leakage resulting from sharing the model parameters. Simultaneously, we employ an anonymity strategy to generate verifiable anonymous identities, which enhances identity privacy protection. Extensive experimental results based on the KunPeng operating system demonstrate that FL4C^2 achieves superior performance while effectively preserving user privacy. © 2023 SPIE.

关键词： Cloud computing

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Simulation of the Design of a Compressed Air Launch System Based on AUTODYN 3

Simulation of the Design of a Compressed Air Launch System B...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Li, Junchen Sun, Yanfu Zhu, Jianyu Equipment Engineering College Shenyang Ligong University Liaoning Province Shenyang110159 China

ISBN: (纸本)9781510664814

With the development background of compressed air launcher for single weapon cartridge, the compressed air launch system and internal ballistic calculation simulation model design with different drain diameter is established by using fluid-solid coupling AUTODYN software, relying on the theory related to the effective energy of compressed air, the mathematical model built is calculated and simulated. The simulation results show that: at 70mm drain diameter, the acceleration obtained by the launcher is 8.2m/s2and reaches the maximum value, the initial velocity of the launcher, which meets the requirements of weapon combat performance. The application of cold energy compressed air in the field of man-portable weapons firing has some feasibility. © 2023 SPIE.

关键词： Compressed air

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Performance Analysis of machine learning Algorithms Using Bagging Ensemble Technique for software Fault Prediction 6

Performance Analysis of Machine Learning Algorithms Using Ba...

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6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Samantaray, Roshan Das, Himansu Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350346961

software development comes with a lot of challenges. Developers face various issues with performance and bugs. These issues increase with the scale of the project and if fewer individuals work on the development. It has become necessary to fix various bugs during development to ensure better performance and reduce the chances of failure during the deployment of the software. As a result of this, faults in the software must be predicted during the earlier stages of development. This would help in reducing the cost of maintenance of the software post-deployment. Multiple software fault prediction (SFP) approaches have been proposed to tackle this problem. These approaches can be improved by implementing ensemble techniques. In this paper, we study the effect of the bagging technique and how it helps to improve the predictive capability across various datasets. These datasets are provided and made open source by NASA. Decision Tree Classifier (DTC), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Support Vector Classifier (SVC) were used as base classifiers for the bagging method. Random forest (RFC) is an ensemble learning algorithm that uses the bagging technique. Based on the outcome of the results, it was concluded that RFC was the best-performing algorithm.. © 2023 IEEE.

关键词： Support vector machines

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Improved the Buddy System to Reduce the System Fargment Created 3

Improved the Buddy System to Reduce the System Fargment Crea...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Zhao, Wenbo Software Engineering Taiyuan University of Technology Shanxi Taiyuan030009 China

ISBN: (纸本)9781510664814

In the OS Kernal, how to allocate disk space is a difficult question. Buddy system is one of important way in various allocating disk space way. We can reduce the fragmentation by Buddy System. However, Buddy System cannot avoid the fragmentation created because the logic in algorithm. How to improve the algorithm is a important way to reduce the fargmentation created and increase the life about system. Because of the Buddy System always merge and split the memory page, fragmenation is created in the process and accumulate in system. With the passage of time, the space which can be used by system will less and less in disk. If we can reduce the number of times of the memory page's merged and spilted in the process of system in used, the fargmentation will be reduce in the system used. © 2023 SPIE.

关键词： Linux

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Estimating Gold Prices in Financial Markets Using machine learning: A Comparative Analysis of Random Forest, K-Nearest Neighbor, and Decision Tree Algorithms

Estimating Gold Prices in Financial Markets Using Machine Le...

引用

2023 Innovations in Intelligent Systems and Applications conference, ASYU 2023

作者： Duman, Sonay Arik, Ecem Turnacigil, Seda Aktas, Mehmet Ali Toros University Department of Software Engineering Mersin Turkey Toros University Department of International Finance and Banking Mersin Turkey

ISBN: (纸本)9798350306590

In the financial markets, gold is a sought-after precious metal due to its use as jewelry and as a trade commodity. Gold is a significant financial asset that can be utilized for evaluating savings in the financial markets. Therefore, for investors who take investment positions based on gold prices, estimate of gold prices is crucial. In this research, the S&P 500 index and the US dollar index were used to estimate gold prices using machine learning. Three different algorithms - Random Forest, K-Nearest Neighbor, and Decision Tree - were tested in this research. The research findings found that the random forest algorithm outperformed other algorithms, with an effective R-square value of 89.77%. © 2023 IEEE.

关键词： Financial markets

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Lightning Nowcasting Based on Gated Depthwise Separable Convolution with Dual-source Meteorological Spatio-temporal Data 7

Lightning Nowcasting Based on Gated Depthwise Separable Conv...

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7th international conference on Electronic Information Technology and Computer engineering (EITCE)

作者： Luo, Qian Luo, Fei Zhang, Xi Zhi, Yaoling Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Guangxi Lightning Protect Ctr Nanning 530022 Guangxi Peoples R China

ISBN: (纸本)9798400708305

Lightning prediction is the task of predicting the future lightning area through historical meteorological observation data. At present, the mainstream methods of lightning prediction include thunder storm recognition extrapolation, numerical model prediction and machine learning lightning prediction methods. In the traditional lightning forecast, it is difficult to find the internal relationship between meteorological data and the generation, development and extinction of lightning through artificially designed equations. There are relatively few machine learning lightning forecasting methods, and the prediction results have the problems of long-time interval and low prediction accuracy. In order to solve the above problems, this paper proposes a gated depthwise separable convolution structure (GDSC) that can realize multi-scale feature fusion. By establishing the global connection of local features, the same effect of attention mechanism is achieved with less calculation. The gated memory unit is used to adaptively determine the importance of each scale feature. On the basis of SimVP network, GDSC is used to improve it, and the SimVP-GDSC model for lightning nowcast is constructed. Experiments are carried out on real lightning data sets, and the results prove the effectiveness of SimVP-GDSC model.

关键词： Lightning Forecast Spatio-temporal Sequence Prediction Depthwise Separable Convolutions Deep learning

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machine learning Based Method for Auditing Personnel Expenses in Public Expenditure 47

Machine Learning Based Method for Auditing Personnel Expense...

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47th IEEE-Computer-Society Annual international conference on Computers, software, and Applications (COMPSAC)

作者： Teixeira, Pedro Henrique Felipe da Silva, Nadia Felix Salvini, Rogerio Univ Fed Goias Inst Informat Goiania Go Brazil Tribunal Comas Municipios Goias Goiania Go Brazil

ISBN: (纸本)9798350326970

This study aims to investigate the use of text classification techniques to support the audit of municipal accounts by the Court of Accounts in the context of calculating Total Personnel Expenditure. The study contributes to the discovery of expenses incorrectly classified by municipal managers due to error or fraud, which would be left out of the calculation. It used data obtained from the Court of Accounts of the Municipalities of Goi ' as, Brazil - TCM. These data were labeled by human experts and then prepared to build expenditure classification models from their description. The TF-IDF algorithm was used for feature engineering, and the Support Vector machines (SVM), Logistic Regression, and Multinomial Naive Bayes were used for classification. The results showed that the proposed method is consistent, having reached an F-Score of 0.91 and 0.97 with the SVM algorithm in the binary and multiclass corpus, respectively. Even dealing with a highly unbalanced dataset in the multiclass approach, the performance can be considered very good. As an innovative study, this work seeks to insert, in the context of the analysis of public expenditure on personnel, the use of text mining techniques to solve problems that would require the effort of many human specialists. The authors understand that there is a practical contribution to the process of analysis of public expenditure on personnel carried out by the Court of Accounts.

关键词： Audit Fiscal Rule Public Spending Personnel Expense machine learning Text Classification

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