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检索条件"任意字段=2015 International Conference on Big Data and Smart Computing, BIGCOMP 2015"

共 2628 条记录，以下是221-230 订阅

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Easy data Augmentation for Improved Malware Detection: A Comparative Study

Easy Data Augmentation for Improved Malware Detection: A Com...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Bae, Jangseong Lee, Changki Kangwon Natl Univ Dept Comp Sci & Engn Chunchon South Korea

ISBN: (纸本)9781728189246

Artificial data generation is important for improving research outcomes when using deep learning. As one of the most popular and promising generative models, the variational autoencoder (VAE) model generates synthetic data for training classifiers more accurately. Artificial data can be generated also via easy data augmentation (EDA) techniques. EDA is a simple method used to boost the performance of text classification tasks, and unlike generative models such as VAE, it does not require model training. Malware detection is a task of determining whether there is malicious software in the host system and diagnosing the type of attack. Without an appropriate amount of training data, the detection efficiency of malicious programs decreases. In this study, EDA was applied to malware detection, and two artificial data generation methods were compared. Using both methods, artificial training data to be used for malware detection were generated, and the long short-term memory recurrent neural network (LSTM RNN) based malware detection classifier was boosted. Experiment results show that when the synthetic malware sample generated by EDA was added to the training data, the accuracy of LSTM RNN classifier improved by 1.76% as compared to the 0.98% improvement by VAE. In addition, EDA could generate malware training data, without requiring a separate training process, 10 times faster than VAE. Further, we performed extensive ablation studies conducted and suggested parameters for practical use.

关键词： Easy data augmentation Variational Autoencoders Deep Learning Long short-term memory recurrent neural network Malware Detection data Augmentation

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Sound Event Detection Via Pervasive Devices for Mobility Surveillance in smart Cities

Sound Event Detection Via Pervasive Devices for Mobility Sur...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Sammarco, Matteo Zeffiro, Trevor Gantert, Luana Campista, Miguel Elias M. Stellantis Amsterdam Netherlands Univ Fed Rio de Janeiro UFRJ GTA PEE COPPE DEL POLI Rio De Janeiro Brazil

ISBN: (纸本)9798350304367;9798350304374

smart cities and Intelligent Transportation Systems rely upon the deployment of sensors in strategic areas for such purposes as crime prevention, urban planning, and road safety. In this paper, we rely on the pervasiveness of smartphones and microphones inside moving vehicles to propose a sound-based event detection system which does not require static sensing infrastructure. We train an embedded Deep Neural Network model able to identify potentially dangerous events like car accidents or emergency vehicles approaching from recorded sounds. We evaluate our model on a large novel dataset of sounds recorded inside the car cabin with audio data augmentation techniques applied thereon. We further evaluate model performance after model quantization, or the addition of environmental noise. Results show an excellent detection accuracy for dangerous events achieving a Matthews Correlation Coefficient (MCC) of 0.95.

关键词： smart city

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PIE Text Encoder: Padding Is Enough to generate Image in diffusion based Text-to-Image Model

PIE Text Encoder: Padding Is Enough to generate Image in dif...

引用

international conference on big data and smart computing (bigcomp)

作者： JongHyun Han SooHyun Lee JongYoul Park Dept. of Applied Artificial Intelligence Seoul National Univ. of Sci. & Tech Seoul Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Although text-to-image models excellently create realistic images from text, they struggle with long-form text due to token limits in the pretrained text encoder. In this paper, we propose the Padding Is Enough(PIE) text encoder that is trained to represent long-form text contexts with a single embedding. The embedding representing long-form text utilizes a knowledge distillation technique, where the outputs from the PIE text encoder and the CLIP text encoder are input into a diffusion model to align their outputs. Specifically, we dont train the diffusion model, but only the text encoder, thereby preserving the extensive pretrained knowledge. Furthermore, the PIE text encoder can be used to extend text prompt in task-specific large pretrained diffusion models. It helps enhance the expressiveness of large pretrained models while reducing costs. We demonstrate that our model achieves high availability as a text extension without damage to the extensive knowledge of the pretrained diffusion models.

关键词： Costs Computational modeling Semantics Text to image big data Diffusion models data models Guidelines

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Lazy Node-Dropping Autoencoder

Lazy Node-Dropping Autoencoder

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international conference on big data and smart computing (bigcomp)

作者： Jaewon Lee Myung Jun Kim Hyunjung Shin Dept. of Artificial Intelligence Ajou University Suown South Korea Soda INRIA Saclay Palaiseau France Dept. of Industrial Engineering Dept. of Artificial Intelligence Ajou University Suwon South Korea

Autoencoders are widely used for dimensionality reduction nonlinearly. However, determining the number of nodes in the autoencoder embedding space is still a challenging task. The number of nodes in the bottleneck layer, which is an encoded representation, is estimated and determined by users. Therefore, to maintain embedding performance and reduce the complexity of the model, an indicator that automatically selects the number of bottleneck nodes is needed. This study proposes a method for automatically estimating the adequate number of nodes in the bottleneck layer while training the model. The basic idea of the proposed method is to eliminate lazy nodes which rarely affect the model performance based on the weight distribution of the bottleneck layer. Since the proposed method takes place in the learning process of the autoencoder, it has the advantage of accelerating the training speed. The proposed method showed better or similar performances in classification accuracy.

关键词： Training Dimensionality reduction Computational modeling Estimation Batch production systems big data Complexity theory

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From data to Decisions: Enterprise-Level Domain-Specific Graph Retrieval-Augmented Generation Systems for Advanced Question Answering

From Data to Decisions: Enterprise-Level Domain-Specific Gra...

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international conference on big data and smart computing (bigcomp)

作者： Sandeep Varma Shivam Shivam Sarun Natarajan Ankita Banerjee Sourodeep Roy ZS Associates Pune India Rockwell Automation Pune India

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Retrieval-Augmented Generation (RAG) is aimed at improving the functionality of large language model (LLM) applications by incorporating specific data. This may include searching for relevant materials or files concerning a particular issue or search provided as background information to the LLM. Some prominent architectures like the Vanilla RAG architecture focuses primarily on retrieval of textual data, primarily utilizing vector databases, thus neglecting the structural intricacies of textual data, resulting in a critical gap in the generation process. To address this gap, we have introduced SAGE-QA (Scalable Advanced Graph RAG for Enterprise Question Answering), which significantly enhances both the retrieval and generation processes by emphasizing the importance of topological information for reasoning tasks on a variety of data sources. For our use case, SAGE-QA significantly outperforms current state-of-the-art RAG methods with its novel retrieval mechanism, which effectively mitigates hallucinations, and acts as a sustainable contextually relevant application.

关键词： Measurement databases Soft sensors Large language models Computational modeling Retrieval augmented generation Computer architecture Question answering (information retrieval) Vectors data models

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A Machine Learning Approach to Government Business Process Re-engineering

A Machine Learning Approach to Government Business Process R...

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international conference on big data and smart computing (bigcomp)

作者： Agus Riyadi Mate Kovacs Uwe Serdült Victor Kryssanov Graduate School of Information Science and Engineering Ritsumeikan University Japan College of Information Science and Engineering Ritsumeikan University Japan Center for Democracy Studies Aarau (ZDA) University of Zurich Switzerland

Governments around the world accumulate large amounts of data but rarely use them to make their daily work more effective. For example, data classification tasks are typically performed manually or with systems that utilize rules created by humans. Public sector business processes are thus often outdated and require significant adaptations. One possible approach to move away from current practices is to apply business process re-engineering (BPR). This study proposes a framework for integrating machine learning into government BPR and using big data sets to optimize current public administration procedures. A case study on expenditure data classification was conducted with textual documents from Indonesian local governments. Several different deep learning approaches were examined. The results obtained confirmed that using the proposed framework leads to significant improvements, compared to the traditional labeling method.

关键词： Local government Finance Manuals big data Semisupervised learning data models Business process re-engineering

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Survival Sequences: Win Prediction from a Strategy Sequence Approach

Survival Sequences: Win Prediction from a Strategy Sequence ...

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international conference on big data and smart computing (bigcomp)

作者： Chaeyeon Sagong Huy Kang Kim School of Cybersecurity Korea University Seoul Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

An important characteristic of battle royale games like PUBG is that the safe zone shrinks as the game phases progress. This makes a player’s phase-by-phase strategy critical to their survival. Existing PUBG win prediction studies rely on post-game statistics, which cannot capture the dynamic environment of the game. To overcome this limitation, we proposed two methods. First, we analyzed player behavior from two perspectives: Fight Element and Active element, with a focus on phase-by-phase adaptation. Second, we propose a deep learning win prediction model that utilizes the strategies derived at each phase. Using massive game log data from PUBG, we conducted a data-driven analysis to derive the relationship between phase strategy and eventual win, which we then used in a win prediction model. We trained three time-series deep learning models LSTM, GRU, and RNN to learn strategy sequences over the phases. All three models performed well in predicting wins, with over 88% accuracy.

关键词： Deep learning Adaptation models Analytical models Accuracy Computational modeling Games Predictive models big data data models Long short term memory

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Simulated Intensity Rendering of 3D LiDAR using Generative Adversarial Network

Simulated Intensity Rendering of 3D LiDAR using Generative A...

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IEEE international conference on big data and smart computing (bigcomp)

作者： Mok, Seung-chan Kim, Gon-woo Chungbuk Natl Univ Dept Elect Engn Cheongju South Korea

ISBN: (纸本)9781728189246

In autonomous vehicle, LiDAR is one of the most importent sensor for measurement range. In particular, LiDAR is widely used in mobile mapping systems (MMS) for building high -definition (HD) map. Not only range but also intensity are useful data for extracting road marking and surface data from sensor. However, LiDAR intensity is affected by surface reflecting characteristic as well as range, incidence angle, atmospheric transmittance and others. Therefore, LiDAR intensity model is difficult to reproduce in simulation environments. In this paper, we proposed realistic intensity rendering method with simulator by generative adversarial network (GAN) trained with unannotated real data. We develop LiDAR projection preprocessing method for operating convolution networks. Our approach have advantage to preserve sensor characteristic and to reduce projection scale loss. We modified CycleGAN architecture to render intensity with unpaired real and synthetic data. Our rendered results show possibility of image-based approach for LiDAR and potential for generating LiDAR dataset by using simulator. Especially, we expect to utilize for military vehicle that has difficulty to reproduce the environments.

关键词： Autonomous Vehicle LiDAR Generative Adversarial Network data Augmentation Simulator

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Extracting Time Information from Korean Documents

Extracting Time Information from Korean Documents

引用

international conference on big data and smart computing (bigcomp)

作者： Seung-Dong Lee Young-Seob Jeong Department of Computer Engineering Chungbuk National University Cheongju South Korea

Most of the documents or writings we see in our daily lives contain information about time, and it has been important to develop a model for extracting the time information from unstructued texts. As the time information appears differently in different languages, there have been studies that proposed language-specific models. This study aims to extract timex3 tags of time expresisons (e.g., May 30) and event tags of eventual expressions (e.g., go, expect) from Korean documents. The Korean dataset used in this study is manually annotated, and used to finetune a pre-trained language model for the extraction task of timex3 and event tags. By experiments, we found that the model gave better performance when it is finetued for extracting the two tags simultaneously. The model achieved 0.5563 of F1 score.

关键词： Computational modeling Computer architecture Writing big data data models data mining Labeling

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Financial data Generation Utilizing Graph Information from Transaction Networks

Financial Data Generation Utilizing Graph Information from T...

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international conference on big data and smart computing (bigcomp)

作者： Kim Kwanggeun Seonkyu Lim Seungho Choi Suk-Ju Kang Sogang University Seoul South Korea Korea Financial Telecommunications and Clearings Institute Seoul South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

High-quality data is crucial for the advancement of machine learning and deep learning models. However, in the financial domain, the amount of data available for training models is limited due to privacy concerns and financial regulations. To address these challenges, the financial industry has increasingly turned to synthetic data generation methods. Previous approaches to generating synthetic financial data, including GAN-based models, have focused on replicating the properties of tabular data. However, they do not perform well in preserving complex network structures, such as degree centrality, which are a significant information of financial transactions. To address this limitation, we propose a novel method for synthetic data generation using Augmented Financial Network (FNet), a self-developed module to preserve the degree centrality of the original data. By applying our FNet to existing GAN-based models, we generate synthetic data that accurately captures both the network structures and tabular properties of real-world financial transactions. Experimental results demonstrate that incorporating FNet into GAN-based models significantly improves fraud detection performance.

关键词： Training Deep learning data privacy Computational modeling Network analyzers data models Regulation Fraud Financial industry Synthetic data

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