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

共 3233 条记录，以下是3151-3160 订阅

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Reentrancy Vulnerability Detection and Localization: A Deep learning Based Two-phase Approach 22

Reentrancy Vulnerability Detection and Localization: A Deep ...

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Proceedings of the 37th IEEE/ACM international conference on Automated software engineering

作者： Zhuo Zhang Yan Lei Meng Yan Yue Yu Jiachi Chen Shangwen Wang Xiaoguang Mao School of Big Data & Software Chongqing University China College of Computer National University of Defense Technology China School of Software Engineering Sun Yat-sen University China

ISBN: (纸本)9781450394758

Smart contracts have been widely and rapidly used to automate financial and business transactions together with blockchains, helping people make agreements while minimizing trusts. With millions of smart contracts deployed on blockchain, various bugs and vulnerabilities in smart contracts have emerged. Following the rapid development of deep learning, many recent studies have used deep learning for vulnerability detection to conduct security checks before deploying smart contracts. These approaches show effective results on detecting whether a smart contract is vulnerable or not whereas their results on locating suspicious statements responsible for the detected vulnerability are still unsatisfactory. To address this problem, we propose a deep learning based two-phase smart contract debugger for reentrancy vulnerability, one of the most severe vulnerabilities, named as ReVulDL: Reentrancy Vulnerability Detection and Localization. ReVulDL integrates the vulnerability detection and localization into a unified debugging pipeline. For the detection phase, given a smart contract, ReVulDL uses a graph-based pre-training model to learn the complex relationships in propagation chains for detecting whether the smart contract contains a reentrancy vulnerability. For the localization phase, if a reentrancy vulnerability is detected, ReVulDL utilizes interpretable machine learning to locate the suspicious statements in smart contract to provide interpretations of the detected vulnerability. Our large-scale empirical study on 47,398 smart contracts shows that ReVulDL achieves promising results in detecting reentrancy vulnerabilities (e.g., outperforming 16 state-of-the-art vulnerability detection approaches) and locating vulnerable statements (e.g., 70.38% of the vulnerable statements are ranked within Top-10).

关键词： Smart contract fault localization reentrancy vulnerability vulnerability detection

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Proceedings of the 14th international conference on Subject-Oriented Business Process Management, S-BPM ONE 2023

Proceedings of the 14th International Conference on Subject-...

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Proceedings of the 14th international conference on Subject-Oriented Business Process Management, S-BPM ONE 2023

ISBN: (纸本)9783031402128

The proceedings contain 19 papers. The special focus in this conference is on Subject-Oriented Business Process Management. The topics include: Improving Interoperability in the Exchange of Digital Twin Data Within engineering Processes;credit to machine learning – Performance of Credit Card Fraud Detection Models;Comparing BPMN 2.0 and PASS: A Review and Analysis of Previous Research;Modelability of Agile Development Projects - An Assessment of the Opportunities and Limitations of BPMN and S-BPM;Proposal for a Recursive Interpreter Specification for PASS in PASS;handling Cross-Cutting Concerns in Subject-Oriented Modeling: Exploration of Capabilities and an Aspect-Oriented Enrichment;Autonomy as Shared Asset of CPS Architectures;Using OPC UA for Integrating and Tracing Data Flows in the Insurance Industry;code Generation for Cloud-Based Implementation of Public Sector Processes Using a Pattern-Based Approach;Revisiting the ALPS - An Investigation of Abstract Layered PASS;Boosting the Maturity of Agile Process Teams: A Complete Model for Assessing and Increasing Self-Organization in BPM;Exploring Potential Barriers for the Adoption of Cognitive Technologies in Industrial Manufacturing SMEs – Preliminary Results of a Qualitative Study;business Process Automation for Data Teams - A Practical Approach at Handelsblatt Media Group;An Approach to Create a Common Frame of Reference for Digital Platform Design in SME Value Networks;the Role of Stories in software Development and Business-Process Modeling;can a ‘Metaverse by Design’ Benefit from Digital Process Twins?;approach of Partial Front-Loading in Engineer to Order.

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Implement Deep learning Networks with Transfer learning to Develop Energy-friendly Applications Supporting Sustainability on Image-based Plant Disease Classification 22

Implement Deep Learning Networks with Transfer Learning to D...

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Proceedings of the 2022 4th international conference on software engineering and Development

作者： Yihang Hu Zhuoran Wang Li Zhu Wenyu Zhang College of Art and Science University of North Carolina at Chapel Hill USA Maynooth International Engineering College Fuzhou University & Maynooth University China Arts and Science Queen's University Canada Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781450397940

Food security is always one of the most important factors in human lives, and crop diseases are one of the major threats which may bring potential damage. Nowadays, with the proliferation of smartphones and the advancement of machine learning methods, it is more likely to achieve rapid identification of disease diagnosis by a smartphone-assisted application supported by deep learning trained models. By comparing different datasets and different kinds of CNN frameworks, this paper trained deep convolutional neural networks based on plant leaves’ images to identify species and detect diseases. Furthermore, this paper found the best combination of different datasets with the highest accuracy. The highest accuracy this work got is 97.37%, using ResNet-9 along with Transfer learning. Nevertheless, these training datasets are too straightforward to deal with the more complex real-world situation. Besides, two-dimensional datasets from time to time have such limited information; therefore, more information is needed to diagnose plants’ diseases. For future extension, this work can apply not only image datasets but also environmental factors, such as soil structure and image background, to construct a more precise model to diagnose plant diseases. Hence, the concept of Point Cloud will be discussed in this paper. This work can be viewed as the first step to build an Energy-friendly plant disease classification application supporting sustainability.

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Dancing, not Wrestling: Moving from Compliance to Concordance for Secure software Development 22

Dancing, not Wrestling: Moving from Compliance to Concordanc...

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Proceedings of the 37th IEEE/ACM international conference on Automated software engineering

作者： Debi Ashenden Gail Ollis Iain Reid School of Computer Science University of Adelaide Australia and School of Computing University of Portsmouth United Kingdom School of Computing University of Portsmouth United Kingdom School of Criminology & Criminal Justice University of Portsmouth United Kingdom

ISBN: (纸本)9781450394758

Secure software development has become an increasingly important focus for research in recent years, not least because of advances in technology such as AI, machine learning (AI/ML), robotics, and autonomous systems (RAS). AI/ML and RAS facilitate automated decision-making and have the capability to have a significant impact on society. As such this technology needs to be trustworthy, and secure software development is a key attribute for trustworthiness. software developers frequently have responsibility and accountability for delivering secure code but limited authority over how this is achieved. Authority tends to lie with cyber security professionals who mandate security processes, tools and training, often with limited success. Our research objective was to better understand how to bridge this gap between software developers and cyber security practitioners so that authority, responsibility and accountability are shared equally. We took inspiration from healthcare research that looks at the relationship between compliance, adherence and concordance. We use this research as a lens through which to analyse qualitative data from 35 interviews with professional software developers. Our research suggests that if software developers and cyber security professionals move to a point of concordance in their interactions it could lead to the negotiation of more realistic cyber security solutions, as well as removing friction from the practice of software developers and ultimately lead to more secure and trustworthy systems.

关键词： Social Practice Security Concordance software Developers

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Estimating the Water Level and Bathymetry of Lake Yahuarcocha, Ecuador Using ICESat-2/ATL13 Satellite Laser Altimetry, System Dynamics Model, and machine learning 12th

Estimating the Water Level and Bathymetry of Lake Yahuarcoch...

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12th international Advanced Computing conference, IACC 2022

作者： Fernando, Garrido Pedro, Granda Department of Software Engineering and Artificial Intelligence Faculty of Engineering in Applied Sciences Technical University of the North Avenue 17 de Julio 5-21 Ibarra Ecuador

ISBN: (纸本)9783031356407

Monitoring the lakes and reservoirs is crucial in managing water resources due to climate change and human interventions, where available data sets on water levels and volumes are scarce. This study aims of predicting the water level height and bathymetry of lake Yahuarcocha, Imbabura, Ecuador with the use of the NASA Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) ATL13/ATL13QL since they provide observations that are suitable for water level estimation, also combining the study with System Dynamics Modelling (SD), and machine learning technique based on the extraction of points from the laser altimetry footprint at the limits of the lake vector, according to the absolute deviation method used to eliminate the outliers of the lake elevation along the orbit, and thus obtain the average values of the water level of the lake. Yahuarcocha has been seriously affected in recent years, and the anthropic activities inherent to the wetlands have been the main cause of the effects, including land use, and other watershed activities influencing surface runoff and groundwaters. Therefore, predictions of lake level are crucial for their sustainable management. In this study, the SD model simulation with the PySD library is used to have the estimation of the level of precipitation, evapotranspiration, and volume of water the lake Yahuarcocha until 2030. The results indicate that the water level estimated from ATL13 is the same as the water level trend measured in situ, with an average estimation error of 30 cm, indicating that the ICESat-2 ATL13 laser altimetry data has high precision, thus the approach of the proposed reaches more than 95.57%. © 2023, Springer Nature Switzerland AG.

关键词： Lakes

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Enhancing Resource Scalability in Cloud Computing: A Comparative Investigation of Neural Network Predictive Techniques 1st

Enhancing Resource Scalability in Cloud Computing: A Compara...

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1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Damarla, Yaswanth Kumar Deenadayal, Bhagya Rekha Jawaharlal Nehru Technological University Kakinada - University College of Engineering Vizianagaram Vizianagaram India Vasireddy Venkatadri Institute of Technology Guntur India

ISBN: (纸本)9783031756078

The field of cloud computing is changing in field of computer science. software companies are using cloud services to deploy their applications. As a result, a lot of interest has been shown by researchers in this field. An important challenge faced in this field is the question of how and when to scale the use of hardware and virtual machine resources. As cloud services are costly, making efficient use of them is necessary to prevent wastage of resources and which will help companies in preserving there business. In this paper, we present a parameter value predictor using LSTMs and RNNs, both of then different predictive mechanisms using neural networks and finding out which is more effective. Previously, approaches like different processing, mathematical models were used to scale microservices. Those approaches were not optimal as they are not good at predicting loads and hard to manage. So, we use Reinforcement learning, namely Double Deep Q-Networks Algorithm, as this approach automatically monitors the parameters which are essential for scaling and triggers the effect (action) onsite immediately. It also balances the acts of exploiting the environment and exploring the environment, using the computing resources and computing cost-effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud platforms

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An efficient network operation automation scheme using network status information for local 5G networks

An efficient network operation automation scheme using netwo...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Mahnsuk Yoon Jihun Seo Kyucheol Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Center for Connected & Automated Driving Research Department of The Fourth Industrial Revolution & Transport Korea Transport Institute Sejong Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

5G SA communication technology is being recognized as a crucial technology shaping the future of wireless communication. Compared to previous generations of networks, 5G offers ultra-high-speed, low latency, and massive connectivity, presenting both enterprises and individual users with opportunities for new business models and an enhanced quality of life. One of the most significant changes between the conventional mobile communication environment and 5G is the activation of various application cases in B2B services based on the local 5G network. The local 5G network, tailored to specific sites, requires effective management of high-volume data generated in dense areas and necessitates minimizing manual intervention by administrators. The ZSM (Zero-touch Service Management) technology is gaining attention to meet these requirements. ZSM focuses on automated operations for managing large systems like commercial mobile communication networks. It is anticipated that ZSM will also be applied to the local 5G network for efficient management and operation of tailored services. This paper proposes a network management technique that minimizes administrator intervention in the local 5G network environment. The proposed network management technique collects statistical logs from local 5G network base stations and core systems, supports real-time network status checks as well as traffic predictions using machine learning. Additionally, it offers automated operation techniques for the local 5G network by incorporating functions such as disconnecting network access for devices that are factors of malfunction within the specialized network.

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Smart Telemedicine Using IoT by Integrating 5G and Block-Chain Techniques

Smart Telemedicine Using IoT by Integrating 5G and Block-Cha...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Shailee Lohmor Choudhary Rinku Sharma Dixit Deepak Das K. Ranjith Singh V. Dinesh Babu Department of Artificial Intelligence & Machine Learning - Data Science New Delhi Institute of Management New Delhi India Agricultural Engineering Karunya Institute of Science and Technology Coimbatore India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Karpagam Institute of Technology Coimbatore India

There are many medical applications for the Internet of Health Things (IoHT). In order to offer patients with more intelligent and effective health diagnoses, contemporary IoHT incorporates wellness items such sensors with continuously monitored medical equipment for the evaluation and handling of a medical chart. Using the combination of 5G networks and ethereum technology, we presented the IoT using a fog medical decision assistance system in this study for the forecast and monitoring of illness along with its severities. Due to its visibility, a frame provides a method for keeping and exchanging knowledge which is safe. Ethereum has various uses in the medical industry and therefore can enhance monitoring equipment, clinical study storage systems, mobile medical software, exchanging and keeping of digital media files, and insurer details stockpiling. The suggested structure will assemble a cloud provider will hold client information from the health equipment that's going to be connected to the individual, along with any pertinent health records. The use of 5G with cryptocurrency technologies allows the reliable transfer of patient information at high transmission rates with short reaction times. In addition, a Deep Neural (NN) classification is employed to forecast illnesses as well as the degree of underlying severity. The suggested model is tested using a variety of classifiers. In order to determine which classification is appropriate for the information, the effectiveness of various classifier is compared by contrasting the results. The NN improved a 98.98 average accuracy. This NN is additionally trained on the information such so this can forecast the outcome of the information category that carries no labels. A taught Naive Bayes is more accurate than conventional classifications at making predictions and effectively displaying the findings.

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Academic performance forecasting model based on artificial intelligence at the Faculty of engineering - Systems and Informatics of the Continental University 23

Academic performance forecasting model based on artificial i...

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Proceedings of the 2023 international conference on Mathematics, Intelligent Computing and machine learning

作者： Elena Sonia Paula Espinoza Rios Felipe Néstor Gutarra Meza José Porfirio González Farías Universidad Continental Peru Tecnológico Nacional de México en Celaya Mexico

ISBN: (纸本)9798400709258

The research established a central objective to implement an academic performance forecasting model based on artificial intelligence to determine the level of incidence factors in the management of academic performance and design the predictive model based on artificial intelligence with the use of classifiers and exploratory analysis to show the distribution of variables and identify patterns where interventions should be made to have better academic performance. The present research has a quantitative approach, correlational scope, non-experimental cross-sectional design, non-parametric statistics, working with a sample of 317 university students, and a diagnostic instrument with Cronbach's α of 83.8%. We worked with the Weka software and the Bayesian classification algorithm J48, obtaining an accuracy level of 76% in predicting academic performance, indicating the influential factors for high academic performance. The results show a weak significant correlation between academic performance and factors. It was concluded that through the use of the artificial intelligence forecast model, the university can identify the influential factors in academic performance, and they can improve it with extracurricular activities and work with the least significant factors through training or psychological well-being workshops to avoid dropout rates and thus have good performance and the development of their capabilities.

关键词： Artificial intelligence academic performance classifiers external factors forecasting model internal factors

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Topic Modeling Approaches—A Comparative Analysis 22nd

Topic Modeling Approaches—A Comparative Analysis

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22nd international conference on Hybrid Intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Lakshminarayana Reddy, D. Shoba Bindu, C. Research Scholar Department of Computer Science and Engineering JNTUA Andhra Pradesh Anantapuramu India Department of Computer Science and Engineering JNTUACEA Andhra Pradesh Anantapuramu India

ISBN: (纸本)9783031274084

Valuable information from a corpus for a specific purpose can be obtained by finding, extracting, and processing the text through text mining. A corpus is a group of documents and the documents could be anything from newspaper articles, tweets, or any kind of data that needs to study. For processing and understanding the structure of a corpus, a technique in text mining is Natural Language Processing (NLP). The study of a corpus in different fields like bioinformatics, software engineering, sentiment analysis, Education, and Linguistics (scientific research) is a challenging task as it contains a vast amount of data. Thus for latent data identification, establishing connections between data and text documents needs topic modeling. Evolutions of topic models are analyzed in this paper from 1990 to present. To better understand the topic modeling concept, a detailed evaluation of techniques is discussed in detail. In this study, we looked into highly scientific articles from 2010 to 2022 based on methods used in different areas to discover current trends, research development, and the intellectual structure of topic modeling. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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