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检索条件"任意字段=8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023"

共 155 条记录，以下是21-30 订阅

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An Approach to Reveal Unknown Malware Hiding Techniques 8

An Approach to Reveal Unknown Malware Hiding Techniques

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8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Shan, Zhiyong Channu, Harsha Vardhan Reddy Wichita State University School of Computing United States

ISBN: (纸本)9798350373615

Security research on smart devices mostly focuses on malware installation and activation, privilege escalation, remote control, financial charges, personal information stealing, and permission use. Less attention has been paid to the deceptive mechanisms, which are critical for the success of malware on smart devices. Generally, malware first gets installed and then continues operating on the device without attracting suspicion from users. To do so, smart device malware uses various techniques to conceal itself, e.g., hiding activity, muting the phone, and deleting call logs. In this work, we developed an approach to semi-Automatically reveal unknown malware hiding techniques. First, it extracts SMH behaviors from malware descriptions by using natural language processing techniques. Second, it maps SMH behaviors to SMH-related APIs based on the analysis of API documents. third, it performs static analysis on the malware apps that contain unknown SMH behaviors to extract the code segments related to the SMH API calls. For those verified SMH code segments, we describe the techniques used for unknown SMH behaviors based on the code segments. Our experiment tested 119 malware apps with hiding behaviors. the F-measure is 85.58%, indicating that our approach is quite effective. © 2023 ieee.

关键词： Android (operating system)

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Analyzing the Growth Patterns of Github Projects to Construct Best Practices for Project Managements 8

Analyzing the Growth Patterns of GitHub Projects to Construc...

引用

8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Kaide, Kaito Tamada, Haruaki Graduate School of Kyoto Sangyo University Division of Frontier Informatics Kyoto Japan Kyoto Sangyo University Faculty of Information Science and Engineering Kyoto Japan

ISBN: (纸本)9798350373615

the Git platforms, such as Github, are big data providers that store various development histories. then, many researchers analyze data from such platforms from various aspects. Recently, AI-based systems have been used to construct themselves. However, there are no studies to measure the quality of the projects, and the ideal images of the projects need to be defined. this paper aims to find the ideal images of the projects in OSS (Open Source Software). For this, we extract the time-series project metrics from the famous OSS projects to categorize for detecting the patterns of projects' growth. Our approach tries to explain the patterns and to give the decision for the patterns, good or not. the time-series metrics from projects include the number of stargazers, forks, commits, etc. the number of stargazers should increase as time passes, and the number of forks tends to decrease. the stargazer pattern indicates that many developers watch the repository since it was managed well. We conducted the case study to analyze the time series data from 10 repositories in Github. As a result, we found that the transitions of the number of issues typically form the sawtooth wave. the sawtooth wave pattern suggests that new issues are reported continuously and inventoried periodically. therefore, the projects with the patterns indicate they are managing well, and many developers pay attention. © 2023 ieee.

关键词： Time series

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Polyp Instance Segmentation in Colonoscopy Images: A Benchmark 8

Polyp Instance Segmentation in Colonoscopy Images: A Benchma...

引用

8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Nguyen, Tan-Cong Phung, Kim-Anh Dao, thao thi Phuong Shen, Ju Nguyen, Tam V. Tran, Minh-Triet University of Social Sciences and Humanities University of Science Vietnam National University Ho Chi Minh City Viet Nam PrimeLabs Oh United States University of Science Vietnam National University Thong Nhat Hospital John von Neumann Institute Ho Chi Minh City Viet Nam University of Dayton DaytonOH United States University of Science John von Neumann Institute Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350373615

Colonoscopy has become the most popular technique to detect abnormalities, which are polyps or adenomas in the colon, to prevent them from becoming cancerous. However, the risk of mistakes during clinical examination is significant. therefore, it is necessary to have a support system that provides reliable predictions and helps doctors not to neglect abnormal signs of disease manifestations. While most current datasets and methods only focus on solving the polyp semantic segmentation problem, polyp shape is also one of the crucial factors that help classify and rank the risk of colorectal cancer. therefore, in addition to surveying dataset benchmarks and polyp semantic segmentation methods for analyzing endoscopic images, this paper aims to construct a dataset of endoscopic images with segmentation and shape annotations for each independent polyp instance. the newly built dataset is then used for benchmarking current state-of-the-Art instance segmentation methods. the results show the feasibility of using these methods to detect polyps by shape in endoscopic images that help identify signs of colorectal cancer. © 2023 ieee.

关键词： Image analysis

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. the concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. the methodology relies on the use of deep learning networks and YOLO framework as well as regression models. the YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. these approaches are used to identify groups of pixels that may individually belong to an object. this then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. the purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 ieee.

关键词： Computer vision

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Examining the Influence of Dependency Injection on Software Maintainability 8

Examining the Influence of Dependency Injection on Software ...

引用

8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Sun, Chun-Wei Liao, Chun-Feng National Chengchi University Department of Computer Science Taipei Taiwan National Chengchi University Department of Computer Science and Program in Digital Content and Technologies Taipei Taiwan

ISBN: (纸本)9798350373615

Dependency Injection (DI) is a widely adopted design pattern in the software industry. DI reduces the otherwise necessary coupling between a client and the concrete object providing the service. this decoupling is achieved by delegating the responsibility of object creation to an external DI module, commonly referred to as a DI Container. Nevertheless, DI can introduce certain side effects. To guide the external DI module on the wiring logic, developers must either detail these logics in configuration files or use language constructs specific to the DI implementation in use. In large-scale projects, managing these wiring logics can pose a significant challenge to software maintainability. Currently, the Spring Framework stands as one of the most prominent DI implementations. It provides three methods for describing the wiring logic: XML, Annotation, and Java Class. this paper delves into the effects of these three methods on software maintainability, their influence on software design, and the optimal circumstances for each method's use in managing wiring logic. Our study examines the issues aforementioned using the well-known web application benchmark, JPetStore. We adapt the benchmark application's source code through different wiring logic implementations and evaluate the resultant effects on software coupling metrics. Finally, we explore the implications of these modifications on software coupling and maintainability. © 2023 ieee.

关键词： Maintainability

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DRL Model for Distributed Agent-based IoT on Multi-Access Edge computing for Accident Forecast 8

DRL Model for Distributed Agent-based IoT on Multi-Access Ed...

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8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Seol, Jongho Kim, Jongyeop Kancharla, Abhilash Middle Georgia State University Department of Information Technology MaconGA United States Georgia Southern University Department of Information Technology StatesboroGA United States University of Tampa Department of Computer Science TampaFL United States

ISBN: (纸本)9798350373615

this paper introduces a state-of-the-Art Deep Reinforcement Learning (DRL) model designed for IoT devices in Multi-Access Edge computing (MEC), with a focus on urban accident prediction. the Edge server serves as a central hub, collecting reinforcement learning data from distributed IoT devices and distributing custom deep learning models for reinforcement learning back to these devices, all within the network. the study also addresses privacy concerns by combining Differential Privacy (DP) with Federated Learning (FL) in DRL within MEC environments. DRL combines deep learning and reinforcement learning principles, enabling wireless IoT devices to optimize actions for maximum rewards in their environments. the surge in IoT devices in networks not only generates massive data but also raises privacy concerns. Differential Privacy, rooted in Google's FL technology, safeguards data privacy. IoT devices utilize DRL and FL to improve learning efficiency and uphold privacy policies. the paper outlines how wireless IoT devices accumulate traceable data tables through reinforcement learning within a Grid-world framework. Within the MEC architecture, Edge servers efficiently execute deep neural networks tailored for reinforcement learning data and distribute results to other IoT devices upon request. this paper presents a tailored DRL model for MEC and a federated DRL framework for maintaining differential privacy while handling shared data generated through deep learning in Edge servers. © 2023 ieee.

关键词： Accidents

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Respiratory Sound data Augmentation Method based on Respiratory Physiology 8

Respiratory Sound Data Augmentation Method based on Respirat...

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8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Komoto, Shunsuke Matsunaga, Kazuto Hirano, Tsunahiko Doi, Keiko Matsunaga, Kazuto Nishifuji, Seiji Nakashima, Shota Yamaguchi University Department of Electrical and Electronic Engineering Ube Japan Yamaguchi University Department of Respiratory Medicine and Infectious Disease Ube Japan

ISBN: (纸本)9798350373615

the number of patients with respiratory diseases, e.g., chronic obstructive pulmonary disease (COPD) and interstitial pneumonia, is increasing. these diseases make it difficult for patients to be aware of their own symptoms because the initial symptoms are unlikely to appear. thus, there is increasing demand for systems that gather respiratory information continuously to realize early disease detection with high sensitivity. Machine learning techniques have been proposed to detect abnormal respiratory sounds, and these detection techniques utilize data augmentation methods to improve accuracy. However, their effectiveness has not been investigated. therefore, in this paper, we examine the respiratory sound data augmentation method with high identification accuracy targeting the detection of abnormal respiratory sound. In addition, we propose effective data augmentation methods for respiratory sound based on respiratory physiology. the effectiveness of the data augmentation was verified experimentally by distinguishing between expiratory and inspiratory sounds from the respiratory sounds of 16 subjects. the experimental results demonstrate the effectiveness of data augmentation in respiratory sounds obtained from the neck region. In addition, the identification accuracy increased by 2.5 points compared to conventional augmentation methods, thereby confirming the efficacy of the proposed method in terms of improving accuracy. © 2023 ieee.

关键词： Machine learning

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Leak Detection and Classification of Water Pipeline data Using LSTM Auto-Encoder with Xavier Initialization 8

Leak Detection and Classification of Water Pipeline Data Usi...

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8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Lee, Daehee Choo, Hyunseung Jeong, Jongpil Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Department of Smart Factory Convergence Suwon Korea Republic of

ISBN: (纸本)9798350373615

Modern cities operate extensive water supply networks. In Korea, the water supply network coverage reaches 98.9%, but more than 10% of water is still leaking every year. Previously, leaks in water pipelines were detected using flow meters and pressure meters, but these methods could only detect leaks after they occurred, necessitating on-site investigations to accurately pinpoint the location of the leak. In the United States, aging water and sewage facilities incur economic losses of hundreds of billions of dollars every year. To address this issue, this paper proposes an LSTM (Long Short-Term Memory) Auto-Encoder model, enhanced with Xavier Initialization, that can detect leaks using water pipeline data and automatically identify the location and extent of the leakage. this model achieved 93.7% performance by applying the Xavier Initialization technique to the LSTM Auto-Encoder model and achieved relatively high accuracy compared to the model using only LSTM Auto-Encoder. the results of this paper can serve as an effective predictive tool for monitoring water pipeline leaks. © 2023 ieee.

关键词： Leak detection

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Aspect Level Sentiment Analysis with CNN Bi-LSTM and Attention Mechanism 26

Aspect Level Sentiment Analysis with CNN Bi-LSTM and Attenti...

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8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Lin, Jionghui Najafabadi, Maryam Khanian The University of Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350373615

this paper introduces a novel neural network model for aspect-level sentiment analysis that combines CNN, Bi-LSTM, and attention mechanisms. the aim is to enhance the effectiveness of sentiment analysis specifically when dealing with sentences containing aspect terms. the main goal is to identify the keywords or elements that contribute to predicting sentiment. Traditional sentiment analysis models often struggle to capture the varying sentiments within sentences treating them uniformly. these models tend to treat the sentence as an entity and overlook the significance of where aspect terms are positioned within the sentence. this limitation significantly impacts the precision of sentiment analysis since sentiments, towards aspects can differ significantly within a single sentence. However, this model addresses that issue by combining CNN, Bi-LSTM and attention mechanisms. CNNs focus on features to identify keywords related to aspects while Bi-LSTM considers the overall context through bidirectional processing enabling a comprehensive understanding of aspect sentiments. Additionally, the attention mechanism enhances the analysis by focusing on words and assigning weights according to their significance. this refinement significantly enhances prediction accuracy. the proposed model is evaluated using the SemEval 2014 datasets, which contain restaurant and laptop reviews. It showcases progress in terms of accuracy and computational efficiency when compared to existing sentiment models. the novel neural network model discussed in this paper represents a progress, in analyzing sentiment at the aspect level. the model accurately identifies keywords associated with specific aspect terms, taking into consideration their positions within the sentence. © 2023 ieee.

关键词： Sentiment analysis

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From ML-Infused Algorithms to Financial Agents, transforming Algorithmic Trading Excellence: A Comprehensive Study 8

From ML-Infused Algorithms to Financial Agents, transforming...

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8th ieee/acis international conference on big data, cloud computing, and data science, bcd 2023

作者： Patil, Tanaya Cummins College of Engineering for Women Computer Science Engineering Pune India

ISBN: (纸本)9798350373615

Algorithmic trading has become a crucial component of modern financial markets, using advanced computational methods to execute trades quickly and accurately. this study explores the integration of machine learning (ML) techniques into algorithmic trading to optimize strategies and improve market performance. It examines the innovative use of ARIMA for buy/sell decisions, moving average based signals, and Random Forest classifiers. Analysis of the performances of the above-mentioned algorithms for stock price decisions of classification and prediction have been carried out with the results displayed in a graphical form to analyze them better. It has been inferred that the statistical technique of the method of moving averages outperforms all the other algorithms for stock price or time series prediction. Building upon these results, this study extends its scope by incorporating an automated financial agent developed using the Alpaca API to monitor Britannia stock. the agent employs a strategy based on a 5-minute moving average of closing prices, executing buy orders when the price is 10 cents above the average and selling when it is 10 cents below the average while holding a position. Furthermore, the paper highlights key challenges facing Algorithmic Trading and outlines potential future research directions in this field. © 2023 ieee.

关键词： Time series analysis

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