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

共 2627 条记录，以下是151-160 订阅

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Extended Abstract: Predicting the Morality of a Character Using Character-Centric Embeddings

Extended Abstract: Predicting the Morality of a Character Us...

引用

international conference on big data and smart computing (bigcomp)

作者： Su Young Bae Eun Chong Kim Yun Gyung Cheong Department of AI Sungkyunkwan University Suwon Korea

Story generation and analysis have been researched for decades using AI and Natural Language Processing technology. However, while ethics is becoming essential for developing AI applications, little research deals with morality in the narrative. We present the framework to build embeddings for representing characters in terms of the character’s morality. In addition, we propose a morality judgment task using storybooks. We conduct experiments, and the results suggest that word embedding models can learn a character’s morality.

关键词： Ethics Computational modeling Bit error rate big data data models Natural language processing Artificial intelligence

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Time Series data Classification using the Knowledge-Augmented Transformer Model

Time Series Data Classification using the Knowledge-Augmente...

引用

2025 IEEE international conference on big data and smart computing, bigcomp 2025

作者： Chung, Hyun-Seok Hyun, Sun-Young Lee, Chang-Eun Ha, Young-Guk SmartLabs Seoul Korea Republic of ETRI Daejeon Korea Republic of Konkuk University Department of Computer Science and Engineering Seoul Korea Republic of

Time Series Classification predicts outcomes by analyzing sequential data that changes over time. As the complexity of time series data increases, particularly in scenarios involving dynamic relationships, existing models face limitations. This paper proposes a classification model using an encoder-based transformer with knowledge augmentation to address these challenges. The model preprocesses time-series data into a graph structure, enabling effective learning of intricate relationships. Relational attention and temporal attention enhance focus on critical relationships and time points, respectively. Additionally, symbolic reasoning through knowledge augmentation allows the model to capture complex, implicit information. Experimental results show that the proposed model outperforms existing SOTA model on complex datasets like SC2EGSet. © 2025 IEEE.

关键词： Taxonomies

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Implementaion of 3D Collaborative Object Detection Systems using RGB-D Sensors

Implementaion of 3D Collaborative Object Detection Systems u...

引用

international conference on big data and smart computing (bigcomp)

作者： Sungwoo Jun Jaeuk Baek Seungwon Do ChangEun Lee Intelligent Convergence Reasearch Laboratory ETRI Daejeon Korea

Recently, artificial intelligence technology has been actively applied to the defense field. In this paper, we propose a method for collaboratively detecting objects on 3D coordinates based on data collected from various viewpoints by multiple RGB-D sensors to increase object detection rates in poor warfare environments. In Chapter 2, we describe the structure, design and implementation of our Collaborative Object Detection Systems and present future research directions in Chapter 3.

关键词： Three-dimensional displays Collaboration Object detection big data Sensor systems Artificial intelligence Intelligent sensors

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Advancing Android Malware Detection with Deep Learning & LLMs in big data Ecosystem: A Forward Vision

Advancing Android Malware Detection with Deep Learning & LLM...

引用

international conference on big data and smart computing (bigcomp)

作者： Juan Rodriguez-Cardenas Nazmus Sakib Sweta Sneha Department of Information Technology Kennesaw State University Marietta United States Department of Information Systems Kennesaw State University Kennesaw United States

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

As the Android operating system dominates the mobile ecosystem, its open-source nature has made it increasingly vulnerable to sophisticated malware attacks. Traditional security techniques, such as static and dynamic code analysis, often fail to detect modern malware due to issues like code obfuscation and limited scalability. This paper explores the limitations of these methods and suggests incorporating advanced Deep Neural Networks as more effective solutions for malware detection in Android environments. Additionally, the integration of Large Language Models (LLMs) offers new possibilities for understanding complex malware behaviors and patterns. We also discuss Android’s security architecture, highlighting key vulnerabilities and attack surfaces. Through this study, we suggest a forward-thinking approach to enhancing Android malware detection using machine learning models, envisioning the future of security in the Android ecosystem as big data and artificial intelligence technologies continue to evolve.

关键词： Deep learning Operating systems Large language models Biological system modeling Scalability Ecosystems big data Malware data models Security

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Enhancing the Software Defect Prediction using AutoInt

Enhancing the Software Defect Prediction using AutoInt

引用

international conference on big data and smart computing (bigcomp)

作者： Shivani Jadhav Saranya Manikandan Duksan Ryu Software Engineering Jeonbuk National University Jeonju South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Software defect prediction (SDP) is essential for ensuring software quality and reliability. Transformer architectures, known for their success in vision and NLP, offer significant potential for SDP. This study investigates the application of the AutoInt Transformer model, which excels in capturing complex feature interactions from numerical and categorical data, for predicting defects. AutoInt is evaluated on several SDP datasets and compared to advanced models like GRU, Tab-Net, FT-Transformer, and TabTransformer using standard evaluation metrics. Results demonstrate AutoInt’s superior performance, further validated through effect size measurements. This research highlights the promise of transformer-based techniques for advancing SDP and enhancing software quality assurance.

关键词： Software quality Predictive models Transformers Size measurement data models Robustness Software reliability Numerical models Software measurement Standards

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Investigating Causality in Mobile Health data through Deep Learning Models

Investigating Causality in Mobile Health Data through Deep L...

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international conference on big data and smart computing (bigcomp)

作者： Eun-Yeol Ma Heeyoung Kim Uichin Lee Department of Industrial and Systems Engineering KAIST Daejeon Republic of Korea School of Computing KAIST Daejeon Republic of Korea

Mobile health has emerged as a practical alternative in treating and managing one’s health problems. However, most of the mobile health data are observational data collected through sensors, which makes it difficult to analyze the causality of the delivered interventions through standard regression methods. In this work, we review deep learning models that can be used to estimate the causal effect in raw mobile health data. These models are capable of handling multivariate time series data in estimating the unbiased causal effect given a sequence of treatments.

关键词： Deep learning Computational modeling Time series analysis big data data models Sensors Standards

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Discovering Research Areas from Patents: A Case Study in Autonomous Vehicles Industry

Discovering Research Areas from Patents: A Case Study in Aut...

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

作者： Ko, Jindeuk Lee, Jooyeoun Ajou Univ Dept Ind Engn Suwon South Korea

ISBN: (纸本)9781728189246

Seven research areas introduced by the 'Autonomous Systems' research lab provide research areas required to enable the autonomous vehicle industry. For ensuring the validity of the research areas with the baseline, we apply Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) on the US patents containing 'Autonomous Vehicles' to identify keywords and research areas of relevant technologies. Keyword clustering and TF-IDF are repeatedly applied to the retrieved keywords to further filter out irrelevant words. Coherence values for LSA and LDA are evaluated to determine an adequate number of topics that need to be generated. We found that results from LSA provide a list of technologies already included in the baseline while topics from LDA provide associated keywords to support defining each technology. We conclude the numbers and topics provided by the baseline model closely represent the industry of autonomous vehicles but the identified topics from us provide a significant extension in research areas. The resulting research areas may provide overviews and guidelines on the autonomous vehicles industry for researchers and institutes

关键词： Autonomous Vehicles big data Natural Language Processing Topic Modelling Patents Analysis

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Realistic Indoor Trajectory Generation Based on Delaunay Graph

Realistic Indoor Trajectory Generation Based on Delaunay Gra...

引用

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

作者： Davide, Passaniti Shahrullohon, Lutfillohonov Kwon, Joonho Pusan Natl Univ Sch Comp Sci & Engn Busan South Korea

ISBN: (纸本)9781728189246

Trajectory analysis can be useful to understand pedestrian behaviour in indoor environments. Collecting real data however can be difficult and time consuming, and many publicly available data sets and trajectory generation methods focus on outdoor scenarios. We propose a method for generating realistic indoor trajectory data, taking into account the various restrictions of indoor environments. The result of our experiments, conducted on two museum floor plans, is a system which only requires a set of points of interest and a floor plan picture. A graph is automatically generated using Delaunay triangulation and trajectories can be obtained by simulating a person walking through the graph.

关键词： data generation Delaunay Indoor Trajectory

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data Processing Pipeline of Short-Term Depression Detection with Large-Scale dataset

Data Processing Pipeline of Short-Term Depression Detection ...

引用

international conference on big data and smart computing (bigcomp)

作者： Yonggeon Lee Youngtae Noh Uichin Lee Energy AI KENTECH Jeollanam-do Republic Of Korea School of Computing KAIST Daejeon Republic of Korea

Depression is a common, recurring mental disorder that causes significant impairment in people’s lives. In recent years, ubiquitous computing using mobile phones can monitor behavioral patterns relevant to depressive symptoms in-the-wild. In this paper, we propose data processing pipeline of short-term depression detection using mobile sensor data. We build a group model classified by depression severity for capturing depressive mood in a short-period time to handle data quality and data imbalance problem in a large-scale dataset. We expect the group model to identify and characterize digital phenotype representing each depressive group as a middle step toward personalization.

关键词： Mood Computational modeling Mental disorders Pipelines Depression data processing Ubiquitous computing

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PA-SIE: Pixel-Attack Methodology Factored Selective Image Encryption Scheme for Low Resource Edge computing Systems

PA-SIE: Pixel-Attack Methodology Factored Selective Image En...

引用

international conference on big data and smart computing (bigcomp)

作者： Harshita Gupta Mayank Kabra Asmita Zjigyasu Madhav Rao Thapar University International Institute of Information Technology Bangalore

The transmission of image data over the internet is inevitable considering its effective use-cases in tele-healthcare, online-banking, remote-education, e-commerce, email exchanges, and many others. However secure communication of the image data is still evolving and not standardized yet, but it is desirable owing to various cyber attacks especially adversarial attack leading to the loss of critical information. One of the primary challenges on adopting secure communication is to balance computational and communication cost for encrypting the data but also attain high security. Applying encryption across all pixelated data is likely to increase the communication overhead and reduce the throughput which otherwise becomes bottleneck for some of real-time applications. The large size of ciphertext makes the system highly inefficient. This research proposes an approach based on one-pixel attack factored selective image encryption scheme (PA-SIE) to reduce the size of the ciphertext while ensuring the security of the image data. The proposed approach identifies the sensitive pixels in the image by using the differential evolution algorithm and encrypts them using a preferred encryption scheme, while leaving the remaining pixels unencrypted. To the best of our knowledge, this is the first study to propose this approach for balancing cost and security in image data transmission. This approach significantly reduces the cost of data transfer while ensuring the secure transmission of sensitive image data. The proposed PA-SIE approach offers maximum communication latency improvement and energy savings of 84.65 %, when compared with full Elliptic Curve Cryptography (ECC) applied encrypted image transmission for web traffic over the HTTPS protocol. The proposed approach is a step towards ensuring secure and efficient transmission of image data over the internet from low resource and bandwidth constrained edge-computing systems.

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