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intelligent Assessment Using Variable N-gram Technique 2nd

Intelligent Assessment Using Variable N-gram Technique

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Kar, Sadhu Prasad Chatterjee, Rajeev Mandal, Jyotsna Kumar Univ Kalyani Kalyani W Bengal India Natl Inst Tech Teachers Training & Res Block FCSect 3 Kolkata 700106 India

ISBN: (纸本)9783030402716;9783030402709

Assessment is the fulcrum of any instructing and learning framework. In e-learning, online evaluation is a significant instrument for assessment of the learner that measures knowledge, skill and attitude. Most of the automated evaluation is normally performed using Multiple Choice Questions (MCQs) containing answers and distractors. Be that as it may, MCQ fails to test the comprehensive level of a learner. Nor they can verify the application of knowledge in real-life situation. To measure the comprehensive ability of the learner, responses based on simple sentences or paragraph(s) required to be consequently evaluated utilizing the online tools and strategies. This research proposal employs automated assessment of paragraph(s) using N-gram tuples that eliminates the dependency on pre-defined keywords. In-order to achieve the required level of performance, the proposed system may require multiple iterations.

关键词： learning e-Assessment Technology enabled learning NLP N-gram

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Effective Malicious URL Detection by Using Generative Adversarial Networks 22nd

Effective Malicious URL Detection by Using Generative Advers...

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22nd international conference on Web engineering (ICWE)

作者： Geng, Jinbu Li, Shuhao Liu, Zhicheng Cheng, Zhenyu Fan, Li Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Natl Comp Network Emergency Response Tech Team Co Beijing Peoples R China

ISBN: (纸本)9783031099175;9783031099168

Malicious URL, a.k.a. malicious website, pose a great threat to Web security. In particular, concept drift caused by variants of malicious URL degrades the performance of existing detection methods based on the available attack patterns. In this paper, We conduct an extensive measurement study of the realistic URL and find that the hierarchical semantics feature is suitable for identifying malicious URL. Therefore, we propose URLGAN, a deep neural network model equipped with the hierarchical semantics features, to detect distinguish between malicious and normal URL. Firstly, we embed the entire URL into a hierarchical semantics structure. Secondly, hierarchical semantics features are extracted from the hierarchical semantics structure through BERT. Then, the extracted features are combined with features generated by the generator, similar but slightly different, to enable the condition discriminator to extract the essential difference between normal and malicious URL. Notably, with the features generated by the generator, we enhance the robustness of the system to detect malicious URL variants. Extensive experiments on the public dataset and our data collected from specific targets demonstrate that our method achieves superior performance to other methods and protects specific targets from the susceptibility of malicious URL.

关键词： Web security Deep learning Malicious URL Detection Hierarchical semantics features Concept drift

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Federated learning for Human Activity Recognition on the MHealth dataset 1

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22nd international conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Sanchez, Sergio Machacuay, Javier Quinde, Mario Department of Industrial and Systems Engineering Universidad de Piura Piura Peru Department of Mechanical and Electrical Engineering Universidad de Piura Piura Peru

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Human activity recognition (HAR) tasks are essential for developing context-aware features due to their capacity to generate higher-level contexts and personalisable services. The traditional centralised training approach requires clients to share their data with a server that trains the HAR algorithms. This data transfer raises privacy concerns on the clients’ side regarding data leaks that can be used to obtain personal data. This research work studies Federated learning (FL) as a privacy-preserving alternative to developing HAR algorithms. The centralised and FL approaches are implemented to train a Multi-Layer Perceptron neural network using three different activation functions (Tanh, ReLU and Sigmoid). The experiments use the MHealth dataset to train and test the HAR algorithms. The results are analysed to compare the performance of the centralised and FL approaches, the activation functions outcomes, and the communication rounds needed between the server and the clients to reach competitive results for the FL models. The outcomes are positive and encourage further work in using of FL for HAR. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Machine learning-Based Remaining Useful Life Predictions and Its Application on Predictive Maintenance

Machine Learning-Based Remaining Useful Life Predictions and...

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international Workshop on Research and Education in Mechatronics (REM)

作者： Ala' Khalifeh Suleman AlMeqdadi Electrical Engineering Department German Jordanian University Amman Jordan Computer Engineering Department German Jordanian University Amman Jordan

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

Predictive maintenance marks a significant improvement over traditional methods by preventing failures, minimizing downtime, and reducing costs, especially in aviation where it enhances safety. This paper explores the impact of window size on Remaining Useful Life (RUL) estimation using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset. The performance of Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) is evaluated in handling time-series data, highlighting the importance of effective windowing techniques. Various fixed window sizes (10, 20, 25, 30, 40, 50-time cycles) with consistent overlap were tested to isolate the window size as the primary variable. Results indicate that larger window sizes generally improve the performance of LSTM models by capturing essential temporal dependencies, while CNN models perform optimally with smaller windows. The paper underscores the trade-offs between window size, accuracy, and computational efficiency, offering insights into optimal configurations for reliable RUL predictions in predictive maintenance applications.

关键词： Accuracy Computational modeling Predictive models data models Systems simulation Safety Convolutional neural networks Long short term memory Tuning Predictive maintenance

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Real-Time 3D Object Detection Based on Dynamic Sparse Voxel Transformer in Mining Area

Real-Time 3D Object Detection Based on Dynamic Sparse Voxel ...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Shengdi Sun Zhangyu Wang Runsen Liu Songyue Yang Liyun Wang Wentao Liu State Key Lab of Intelligent Transportation System The Key Laboratory of Autonomous Transportation Technology for Special Vehicles School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements.

关键词： Point cloud compression Three-dimensional displays Runtime Heuristic algorithms Transportation Object detection Transformers Real-time systems Safety data mining

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ConFunc: Enhanced Binary Function-Level Representation through Contrastive learning

ConFunc: Enhanced Binary Function-Level Representation throu...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Longfei Li Xiaokang Yin Xiao Li Xiaoya Zhu Shengli Liu Zhengzhou University Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Binary code similarity detection (BCSD) has numerous applications, including malware detection, vulnerability search, plagiarism detection, and patch identification. Recent studies have demonstrated that with the rapid progress of machine learning (ML) techniques, various BCSD approaches based on machine learning have exhibited stronger performance than traditional methods. However, current ML-based BCSD approaches tend to ignore the issue of training samples, and most ML-based BCSD approaches are based on supervised learning, which is suffered from the labelling difficulties. To mitigate these issues, we propose ConFunc: a function-level binary code similarity detection framework based on contrastive learning. Performance evaluation shows that ConFunc enhances the Mean Reciprocal Rank (MRR) and Recall rates (Recall@1) of baseline models by fully harnessing the potential of the data. Additionally, ConFunc demonstrates stronger performance in scenarios with scarce data, achieving the baseline model’s performance on the entire dataset using only 10% of the complete dataset. In real-world patch identification and vulnerability search tasks, ConFunc consistently outperforms other baseline models in MRR and Recall@10.

关键词： Training Privacy Computational modeling Plagiarism Supervised learning Binary codes Machine learning

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Pollutant Concentration Prediction Based on the Optimization of Long-Short Distance in Space

Pollutant Concentration Prediction Based on the Optimization...

引用

IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Muyao Peng Kun Wang Yueli Wen School of Communication Engineering Xidian University Xi’an China School of Computer Science and Technology Xidian University Xi’an China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As air pollution continues to pose a growing threat to human health and environmental well-being, accurate forecasting of pollutant concentration has become increasingly important. Existing prediction models can be divided into two categories: non-machine learning models and machine learning models. Nonmachine learning models excessively rely on historical data, while machine learning models have high time complexity and require large amounts of data. To solve the above problems, this paper proposes a spatial distance optimization-based model for predicting pollutant concentration. The proposed model uses a neural network for predicting pollutant concentration, with the nonlinear activation increasing the model's ability to fit nonlinear data. Additionally, it proposes a spatial distance optimization strategy that divides the surrounding monitoring stations based on their distance from the target station. As the neural network model deepens, the influence of more distant monitoring stations on the predicted values gradually reduces. The proposed model adapts the weights based on training errors, implicitly learning the distribution of the relationship between station distances and concentration values and fully exploiting the intrinsic information of the data. Simulation results show that the proposed model can achieve a prediction accuracy up to 6.38% higher than traditional methods.

关键词： Training Adaptation models Atmospheric modeling Simulation Machine learning Predictive models data models

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A Public Benchmark of REST APIs

A Public Benchmark of REST APIs

引用

international Mining Software Repositories, MSR

作者： Alix Decrop Sara Eraso Xavier Devroey Gilles Perrouin NADI University of Namur Namur Belgium University of Valle Cali Valle del Cauca Colombia

ISBN: (数字)9798331501839

ISBN: (纸本)9798331501846

In software engineering, benchmarks are widely used to evaluate and compare the performance, functionality, and reliability of analysis tools. Despite the prevalence of benchmarks in areas such as databases, machine learning, and programming languages, there is a notable absence of publicly available benchmarks for REST APIs, a cornerstone of modern web-based systems. While existing research papers occasionally employ similar REST APIs in their evaluations, opportunistic API selection hampers comparison. Moreover, these studies often rely on API documentation and structural characteristics. Without a reliable benchmark, API data used in evaluations may be outdated or inaccurate, compromising reliability and reproducibility. Hence, this paper addresses a gap in the literature by providing a comprehensive and Public REST API Benchmark (PRAB), to be utilized by researchers in their evaluations. The benchmark contains documentation and structural characteristics of 60 publicly available REST APIs. First, we conduct a systematic mapping study to discover the available and public REST APIs that are utilized in the academic literature. Then, by analyzing the resulting APIs, we report their structural characteristics (e.g., routes, query parameters, HTTP methods, authentication). Finally, we provide their documentation (i.e., OpenAPI Specification, Postman Collection) in a publicly available GitHub repository, to help with future evaluations of REST API studies.

关键词： Systematics Documentation Benchmark testing Reliability engineering Software Reproducibility of results HTTP Software reliability Software engineering Software development management

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Privacy issues involved in data mining and countermeasures

Privacy issues involved in data mining and countermeasures

引用

MEMAT 2022;2nd international conference on Mechanical engineering, intelligent Manufacturing and Automation Technology

作者： Jing Liu

data mining can extract valuable information from large amounts of data and has matured in recent years as an important resource for productive life. This technology is a decision support process. It is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, database, visualization technology, etc. It is highly automated to analyse a company's data, make inductive inferences and extract potential patterns from it to help decision makers adjust their market strategies, reduce risk and make the right decisions. Technology is neutral and not entirely good or bad. However, with the rapid development of the Internet, the increasing popularity of embedded and location-based technologies and the widespread use of location-based social networks, the amount of personal privacy information has increased dramatically and it is very easy to violate personal privacy if data mining tools are applied directly to the data for comprehensive analysis. Thus, in order to be able to carry out data mining with privacy protection, this article suggests some solutions, including: 1) Establish a perfect data privacy protection assessment mechanism and legal means.; 2) Develop a new theoretical framework for privacy protection; and 3) Efficient encryption algorithm.

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System-Awareness: An Enabling Condition to Design and Deploy Anomaly Detectors

System-Awareness: An Enabling Condition to Design and Deploy...

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IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Muhammad Atif Tommaso Zoppi Andrea Bondavalli Department of Mathematics and Informatics Viale Morgagni 65 University of Florence Florence Italy

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Decades of research and practice originated mechanisms that can gather data from any system and process them for identifying deviations from the nominal expected behavior of such system i.e., anomalies. Anomaly detection is conducted through Machine learning (ML) algorithms that model the normal behavior of the system using information contained in a training dataset. This process is usually focused on optimizing the ML algorithm, neglecting the fact that an anomaly detector is very often integrated as a component within the system and should not be analyzed in isolation. This paper advocates that anomaly detectors should be i) designed to properly interact with other system components and ii) be aware of the operating context of the system during its operational life. This allows for detecting global and local (contextual) anomalies, enhancing the decision-making process at both component and system-level. We verify our conjecture by crafting and exercising a framework for designing and deploying system-aware anomaly detectors according to our design principles. This allows comparing traditional anomaly detectors against time-aware and system-aware detectors using two datasets obtained by monitoring an IoT device and ROS2-Based cyber-physical system and IoT networks under normal and anomalous operating conditions. Results show the superiority of time and system-aware detectors over their competitors, confirming our conjecture that anomaly detection is a system's business and should not be considered a standalone component.

关键词： Training Machine learning algorithms Software architecture Detectors Reliability engineering Software reliability Internet of Things Modeling Anomaly detection Monitoring

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