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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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Leveraging AI Techniques for Enhanced software Defect Prediction 4

Leveraging AI Techniques for Enhanced Software Defect Predic...

引用

4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Mustaqeem, Mohd Mustajab, Suhel Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9798350369786

In software engineering (SE), software defect prediction (SDP) plays a pivotal role in enhancing software quality by detecting defects during development. However, the challenge of imbalanced datasets significantly impacts the efficiency of defect prediction models. This study leverages Artificial Intelligence (AI) techniques to overcome these challenges and improve defect prediction. We explore ensemble methods, resampling with cost-sensitive learning, and anomaly detection via Transfer learning (TL). Experiments on NASA MDP's PC2 and AEEEM's JDT datasets demonstrate our strategies' effectiveness, particularly in managing imbalanced data. Notable outcomes include a 95% accuracy, 97% precision, 98% recall, and an F1-score of 97% for the ensemble model on the PC2 dataset and similarly with the JDT dataset. These AI-driven techniques represent a substantial advancement in SDP. Furthermore, our One-Class Support Vector machine (SVM) anomaly detection identifies 115 anomalies in the PC2 dataset, with similar results in the JDT dataset. TL broadens predictive capabilities, providing insights from a wider context. These outcomes underscore the significant impact of AI-driven techniques in navigating the complexities of SDP and advancing defect prediction models. © 2023 IEEE.

关键词： Prediction models

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A Study on Early & Non-Intrusive Security Assessment for Container Images 18

A Study on Early & Non-Intrusive Security Assessment for Con...

引用

18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Ul Haque, Mubin Babar, Muhammad Ali Univ Adelaide Ctr Res Engn Software Technol CREST Adelaide SA Australia Univ Adelaide Sch Comp Sci Adelaide SA Australia Cyber Secur Cooperat Res Ctr Joondalup WA Australia

ISBN: (纸本)9789897586477

The ubiquitous adoption of container images to virtualize the software contents bring significant attention in its security configuration due to intricate and evolving security issues. Early security assessment of container images can prevent and mitigate security attacks on containers, and enabling practitioners to realize the secured configuration. Using security tools, which operate in intrusive manner in the early assessment, raise critical concern in its applicability where the container image contents are considered as highly sensitive. Moreover, the sequential steps and manual intervention required for using the security tools negatively impact the development and deployment of container images. In this regard, we aim to empirically investigate the effectiveness of Open Container Initiative (OCI) properties with the machine learning (ML) models to assess the security without peeking inside the container images. We extracted OCI properties from 1,137 real-world container images and investigated six traditional ML models with different OCI properties to identify the optimal ML model and its generalizability. Our empirical results show that the ensemble ML models provide the optimal performance to assess the container image security when the model is built with all the OCI properties. Our empirical evidence will guide practitioners in the early security assessment of container images in non-intrusive way as well as reducing the manual intervention required for using security tools to assess the security of container images.

关键词： Container images configuration Security Non-Intrusive Assessment machine learning

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Practical and Efficient Model Extraction of Sentiment Analysis APIs 23

Practical and Efficient Model Extraction of Sentiment Analys...

引用

45th IEEE/ACM international conference on software engineering (ICSE)

作者： Wu, Weibin Zhang, Jianping Wei, Victor Junqiu Chen, Xixian Zheng, Zibin King, Irwin Lyu, Michael R. Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9781665457019

Despite their stunning performance, developing deep learning models from scratch is a formidable task. Therefore, it popularizes machine-learning-as-a-Service (MLaaS), where general users can access the trained models of MLaaS providers via Application Programming Interfaces (APIs) on a pay-per-query basis. Unfortunately, the success of MLaaS is under threat from model extraction attacks, where attackers intend to extract a local model of equivalent functionality to the target MLaaS model. However, existing studies on model extraction of text analytics APIs frequently assume adversaries have strong knowledge about the victim model, like its architecture and parameters, which hardly holds in practice. Besides, since the attacker's and the victim's training data can be considerably discrepant, it is non-trivial to perform efficient model extraction. In this paper, to advance the understanding of such attacks, we propose a framework, PEEP, for practical and efficient model extraction of sentiment analysis APIs with only query access. Specifically, PEEP features a learning-based scheme, which employs out-of-domain public corpora and a novel query strategy to construct proxy training data for model extraction. Besides, PEEP introduces a greedy search algorithm to settle an appropriate architecture for the extracted model. We conducted extensive experiments with two victim models across three datasets and two real-life commercial sentiment analysis APIs. Experimental results corroborate that PEEP can consistently outperform the state-of-the-art baselines in terms of effectiveness and efficiency.

关键词： model extraction sentiment analysis APIs active learning architecture search

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Spatio-Temporal Missing Data Reconstruction by Using Deep Neural Networks in Agricultural Monitoring Systems 11

Spatio-Temporal Missing Data Reconstruction by Using Deep Ne...

引用

11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Sentop, Mehmet Selahaddin Yucel, Meric Ustundag, Burak Berk Istanbul Technical University Computer Engineering Istanbul Turkey Istanbul Technical University National Software Certification Research Center Istanbul Turkey

ISBN: (纸本)9798350303513

This study addresses the issue of missing, distorted, or inaccurate data in agricultural monitoring systems. Such data issues can lead to errors in prediction and management models, affecting various applications in agricultural meteorology and remote sensing. Conventional missing data completion algorithms often fail to effectively leverage the inherent relationships between temporal and spatial data in agricultural observation systems. machine learning techniques, specifically deep learning, offer a promising solution by considering factors such as time windows, seasons, and plant characteristics to fill missing data. However, managing the non-linear nature of agricultural monitoring within a machine learning framework poses a challenge. This study proposes a new deep learning approach called Predictive Error Compensated Network that addresses missing data reconstruction while mitigating overfitting. Predictive Error Compensated Network utilizes feature extraction networks and Discrete Wavelet Transform to incorporate different data types and time windows, improving performance. Evaluation against traditional methods demonstrated superior results with Predictive Error Compensated Network, achieving a significant reduction in reconstruction Root Mean Squared Error across different time windows. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Artificial Intelligence Chatbots for Customer Service & Their Impacts on Consumer Compliancy 3

Artificial Intelligence Chatbots for Customer Service & Thei...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

ISBN: (纸本)9798350399264

E-commerce utilize to interacting with clients using live chat interface and has gaining popularity which offer live customer care. The interactive software agents, often known as chatbots, which are tools meant to connect with humans by way of human speech and are frequently based on machine learning. This system frequently replaces the human chat program agents (AI). Because of their cost & time-saving prospects which led to a wider adoption of Intelligence chatbots. This system commonly falls short of customer expectation, which may make consumers less likely to follow the chatbot's instructions. Here the work experimentally investigates through a randomised online study how vocal anthropomorphic model cues as well as the foot-in-the-door (FITD) method effect user request conformity, relying on social reaction and dedication theory. System findings show that users are more likely to cooperate with such a chatbot's demand for bringing improvement when it is anthropomorphic and when there is a need to maintain consistency. The outcomes additionally demonstrate that social interaction mediates the impact of humanistic model signals on customer adherence. © 2023 IEEE.

关键词： software agents

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Power Equipment Defect Detection Method Based on CenterNet 8

Power Equipment Defect Detection Method Based on CenterNet

引用

8th international conference on Energy System, Electricity, and Power, ESEP 2023

作者： Zheng, Peihao Tao, Jun Yu, Jiangbin Xue, Meng Liu, Jian Liu, Xiaofei Anhui Jiyuan Software Co. Ltd. No. 1800 Xiyou Road Shushan District Anhui Province Hefei City230022 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The infrared images collected by robots during the inspection process are difficult to reflect the texture information of the equipment. Artificial methods or traditional machine learning methods cannot accurately identify and classify power equipment defects, and other environmental factors can easily lead to misjudgment. In response to this issue, a CenterNet based defect detection method for power equipment is proposed in this paper. This method uses CenterNet and combines structured positioning to collect and train on-site infrared image data samples, achieving high accuracy in identifying and locating different substation equipment and components from complex infrared images. Based on the surface temperature range of equipment components and the identified substation equipment type, combined with relevant temperature specifications, infrared image defect detection of power equipment is achieved. The experimental results show that this method improves the detection accuracy of infrared image defect detection in power equipment. © 2024 SPIE.

关键词： learning systems

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Low-Latency Internet Traffic Identification using machine learning with Trend-based Features 19

Low-Latency Internet Traffic Identification using Machine Le...

引用

19th IEEE international Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Enisoglu, Ramazan Rakocevic, Veselin City Univ London London England

ISBN: (纸本)9798350333398

Identifying the type of network traffic has several advantages, such as detecting and preventing applications that violate an organization's security policy or improving Quality of Service (QoS) and Quality of Experience (QoE) through traffic engineering. To enhance QoS support for Internet Service Providers (ISPs), a fine-grained classification scheme for network traffic is proposed in this paper. Statistical analysis of the throughput patterns of FTP, video conferencing, and video streaming traffic reveal that using new statistical features can be more effective at distinguishing the Internet traffic, especially from a QoS perspective, compared to the features commonly used in the literature, even for encrypted traffic. In this work, machine learning algorithms for classifying the low-latency traffic are trained using combinations of statistical features including the novel trend identification. Experiments are conducted to evaluate the proposed method using large-scale real network traffic data. Results show that our method can classify the particular type of traffic with accuracy of over 97%, and identify the low-latency traffic in the traffic mix with accuracy of 87%.

关键词： Network traffic classification k-NN SVM machine learning Feature selection Internet traffic mix Statistical features QoS Low-Latency

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Adversarial learning Aided Malicious Network Traffic Detection Based on EDE Framework

Adversarial Learning Aided Malicious Network Traffic Detecti...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Zhang, Lanping Wang, Yu Ohtsuki, Tomoaki Adebisi, Bamidele Wu, Hsiao-Chun Gui, Guan Njupt College of Telecommunications and Information Engineering Nanjing China Keio University Department of Information and Computer Science Yokohama Japan Manchester Metropolitan University Faculty of Science and Engineering Manchester United Kingdom Louisiana State University School of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350345384

With the increasing number of new malicious software and network attacks, anomalous network traffic detection technology needs to continuously identify unknown attacks in order to adapt to complex network environments. In this paper, we propose a novel adversarial learning aided malicious network traffic detection (AMD) method based on encoder-decoder-encoder (EDE) framework. The proposed AMD method detects unknown anomalies in a single type of semi-supervised form and converts network traffic data into pictures for later processing. First, an anomalous network traffic detection model consisting of two encoders, a decoder and a discriminator is introduced. Second, by using the visibility of normal samples and the unrecognizability of unknown anomaly samples, the difference between the two samples is enlarged to facilitate effective sample detection. Finally, experiments show that the proposed model detects anomalies in targeted space and is robust to complex and noisy network environments. At the same time, compared to traditional machine learning methods, AMD's shows performance improvement of up to 40%. © 2023 IEEE.

关键词： Anomaly detection

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Boosting Image Classification Accuracy Leveraging Finer Grained Labels 2

Boosting Image Classification Accuracy Leveraging Finer Grai...

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2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Zhu, Lei Nanjing University Department of Software Engineering Nanjing210000 China

ISBN: (纸本)9798350331417

As the landscape of deep learning has evolved rapidly, numerous models and methodologies have emerged, revolutionizing the domain of image classification. In recent years, OpenAI's Contrastive Language-Image Pre-Training (CLIP) model, which uniquely bridges visual and textual information, has demonstrated robust generalization across diverse tasks, presenting fresh avenues and opportunities for image classification. Building upon the capabilities of the CLIP model, this research further explores the possibility that finer grained labels may help improve the accuracy of image classification. The proposed method is divided into three steps. First, determine existing or manually annotated sub-class labels to capture nuanced details within primary categories. Second, use CLIP as a feature extractor, augmented with a fully connected layer. This setup facilitates supervised classification, leveraging the granularity of the identified sub-class labels. Third, the classified sub-labels are mapped back to their parent categories, resulting in the final prediction. By introducing and combining the precision of finer-grained labels with CLIP's robust architecture, this method offers a promising avenue for bolstering classification accuracy. Code is available at https://***/24kcqsn/Image-Classification-Leveraging-Finer-Grained-Labels. © 2023 IEEE.

关键词： deep learning fine grained labels image classification

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Analysis of Sentiments on Russia-Ukraine conflict using machine learning Techniques 1

Analysis of Sentiments on Russia-Ukraine Conflict using Mach...

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Negi, Sakshi Dawar, Ishaan Prakash, Rishabh Tiwari, Prashant Bhushan, Megha Kumar, Ajay DIT University School of Computing Uttarakhand Dehradun India DTU Department of Software Engineering Delhi India KIET Group of Institutions CSIT Department Gaziabad India

ISBN: (纸本)9798350335774

Nowadays, the internet has become an essential part of the current scenario. Due to the rapid expansion of the social network industry, people are using its platforms to share their opinions on current events. It is important to collect and evaluate consumer feedback on products, services, and other topics. It is of utmost importance to determine if an endorsement or criticism of war operations leads to the universal message as the crisis between Russia and Ukraine has worsened and more foreign powers are involved. The abundant writings on the critical connection between public opinion to international relations support the above-mentioned objective. This study focuses mainly on Twitter text data extracted by Twitter API during the Ukraine-Russia war timeline and is available on Kaggle. Sentimental analysis using various supervised machine learning techniques, including Random Forest, Xtreme Gradient Boosting, Support Vector machine, and Logistic Regression. Also, the comparison of their performance based on various metrics, including the accuracy and precision of the data is presented. As a result, it investigates the opinions and reactions of the global world to Russia's invasion of Ukraine. © 2023 IEEE.

关键词： Sentiment analysis

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