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A Method of Identifying Causes of Prediction Errors to Accelerate MLOps

A Method of Identifying Causes of Prediction Errors to Accel...

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4th IEEE/ACM international Workshop on Deep learning for Testing and Testing for Deep learning (DeepTest) / international conference on software engineering (ICSE)

作者： Sakuma, Keita Matsuno, Ryuta Kameda, Yoshio NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350338126

MLOps, techniques for solving operational problems in machine learning, have attracted attention in recent years. In MLOps, understanding why mispredictions occur is essential to improving operational models. However, misprediction analysis is currently a time-consuming process performed manually by data scientists. In this paper, we propose a flowchart-structured analysis method (called AIEDF) that automatically identifies the causes of mispredictions during model operation. Thanks to the flowchart structure, AIEDF's analyses are comprehensive and explainable. In addition, AIEDF is flexible in implementation and can be model agnostic. Through experiments with synthetic and real data, we have demonstrated that AIEDF accurately identifies root causes and provides valuable insights for model improvement.

关键词： MLOps machine learning Explainability

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Encoding Conceptual Models for machine learning: A Systematic Review

Encoding Conceptual Models for Machine Learning: A Systemati...

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ACM/IEEE international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Ali, Syed Juned Gavric, Aleksandar Proper, Henderik Bork, Dominik TU Wien Business Informat Grp Vienna Austria

ISBN: (纸本)9798350324983

Conceptual models are essential in software and Information Systems engineering to meet many purposes since they explicitly represent the subject domains. machine learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works encode models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models' structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model's structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.

关键词： machine learning Model-driven engineering Model Encoding Systematic Literature Review

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A Domain-Specific Language for Monitoring ML Model Performance

A Domain-Specific Language for Monitoring ML Model Performan...

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ACM/IEEE international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Kourouklidis, Panagiotis Kolovos, Dimitris Noppen, Joost Matragkas, Nicholas Univ York British Telecom Ipswich Suffolk England Univ York York N Yorkshire England British Telecommun PLC Ipswich Suffolk England Univ Paris Saclay Paris France

ISBN: (纸本)9798350324983

As machine learning (ML) starts to offer competitive advantages for an increasing number of application domains, many organisations invest in developing ML-enabled products. The development of these products poses unique challenges compared to traditional software engineering projects and requires the collaboration of people from different disciplines. This work focuses on alleviating some of these challenges related to implementing monitoring systems for deployed ML models. To this end, a domain-specific language (DSL) is developed that data scientists can use to declaratively define monitoring workflows. Complementary to the DSL, a runtime component is developed that implements the specified behaviour. This component is designed to be easily integrated with the rest of an organisation's ML platform and extended by software engineers that do not necessarily have experience with model-driven engineering. An evaluation of the proposed system that supports the validity of the approach is also presented.

关键词： machine learning Model-Driven engineering Dataset Shift MLOps

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A Systematic Literature Review on Client Selection in Federated learning 23

A Systematic Literature Review on Client Selection in Federa...

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27th international conference on Evaluation and Assessment in software engineering (EASE)

作者： Smestad, Carl Li, Jingyue Norwegian Univ Sci & Technol Trondheim Norway

ISBN: (纸本)9798400700446

With the arising concerns of privacy within machine learning, federated learning (FL) was invented in 2017, in which the clients, such as mobile devices, train a model and send the update to the centralized server. Choosing clients randomly for FL can harm learning performance due to different reasons. Many studies have proposed approaches to address the challenges of client selection of FL. However, no systematic literature review (SLR) on this topic existed. This SLR investigates the state of the art of client selection in FL and answers the challenges, solutions, and metrics to evaluate the solutions. We systematically reviewed 47 primary studies. The main challenges found in client selection are heterogeneity, resource allocation, communication costs, and fairness. The client selection schemes aim to improve the original random selection algorithm by focusing on one or several of the aforementioned challenges. The most common metric used is testing accuracy versus communication rounds, as testing accuracy measures the successfulness of the learning and preferably in as few communication rounds as possible, as they are very expensive. Although several possible improvements can be made with the current state of client selection, the most beneficial ones are evaluating the impact of unsuccessful clients and gaining a more theoretical understanding of the impact of fairness in FL.

关键词： systematic literature review software metric federated learning client selection neural network

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Proposing Model for Classification of Malicious SQLi Code Using machine learning Approach 1

Proposing Model for Classification of Malicious SQLi Code Us...

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1st international conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Shah, Imdad Ali Jhanjhi, N.Z. Brohi, Sarfraz Nawaz Taylor's University School of Computing Science Malaysia Scs Taylor's University School of Computer Science Malaysia University of Bristol United Kingdom

ISBN: (纸本)9798350348637

In today's digital landscape, we need to pay significant attention to identifying flaws in web-based applications. Most online applications use browsers to access data that belongs to web-based applications. The victim industry and industry losses increase in the perspective of SQL injection attacks. at the same time, the software market size has increased from 2019 to 2023, as the number of SQL injection attacks has grown in the same years. Many database-driven websites work in the market, and there is no mechanism to block another input, and there is a longer chance of injecting malicious SQL code into the database. The hackers injected through string commands and functions of SQL (Structure Query Language) due to the dynamic disregard of this command in the runtime. The security community has developed several methods for detecting software vulnerabilities, such as supervised, semi-supervised, deep learning, and machine learning. Given our literature, successful and effective vulnerability identification models require for the users. OWASP has the top 10-2021 vulnerabilities, as the numbers 07 and 08 were significant code injection attacks found in our literature review as critical, and 20% of detection models work in the software industry on machine learning basis. The review's findings demonstrated that studies create new SQL injection attack datasets using machine learning techniques and *** proposed model is working on a machine learning basis to identify two vulnerabilities of OWASP's top-ten 2021 No: 7 and 8: (1) identification and authentication failures and (2) software and data integrity failures. Both vulnerabilities are related to SQL injection. Further, this model works as a tool for identifying vulnerabilities in input data in the runtime. Support Vector machine (SVM), K-nearest neighbours Random Forest (RF) and Random Forest (RF). RF provides higher accuracy due to its feature extraction and classification process and trained for classificatio

关键词： Adversarial machine learning

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Prediction Model of Students' learning Behavior on learning Effect in Online Live Class Based on machine learning Algorithm 5

Prediction Model of Students' Learning Behavior on Learning ...

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5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Peng, Ling Jiang, Bangwen Guangdong University of Science and Technology Dongguan523038 China

ISBN: (纸本)9781510662520

With the reform of information-based education, online learning has become a regular way of learning. In order to better help online learners to learn and promote the improvement of teachers' online teaching quality. In this study, the machine learning algorithm was used to mine the data such as the online live class learning behavior of university students on the Super Star learning platform through Python software, and the online teaching effect prediction model was constructed to better predict the learning effect evaluation of online learners. © 2023 SPIE.

关键词： Python

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COMPREHENSIVE ANALYSIS OF VARIOUS ATTACKS IN THE IOT ENVIRONMENT

COMPREHENSIVE ANALYSIS OF VARIOUS ATTACKS IN THE IOT ENVIRON...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jaganathan, D. Prabhu, V. Oormila, L. Rajeswari, P. Rajesh, G. Department of Artificial Intelligence Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of CSE RMK Engineering College Tamilnadu Kavaraipettai India Department of CSE Bharath Institute of Higher Education and Research Tamilnadu Selaiyur India Department of Computer Science Mannar Thirumalai Naicker College Pasumalai Tamilnadu Madurai India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Avadi India

ISBN: (纸本)9798350305708

A network of connected objects known as the Internet of Things (IoT) allows data to be transmitted over a network without the need for direct physical contact. Wearable sensors, software, actuators, and computer hardware are all examples of IoT devices. They are associated with a particular thing that is connected to the internet. Meanwhile, security risks have grown as a result of IoT devices' lack of protection. One of the most prevalent devices that can be readily impacted by threads and swiftly propagate across the devices is the Internet of Things (IoT) hardware. This review used machine learning techniques to compare and evaluate the research on past investigations and research into botnet identification. The most effective detective control method against assaults caused by IoT devices is machine learning. Concerns and difficulties related to IoT security are discussed, along with an SDN-based framework for facilitating cooperative IoT security. In order to combat attacks, it is also necessary to provide a structure that can rapidly share information about an attack with peer controllers. In order to build and improve security for IoT systems, this work aims to give a thorough assessment of ML techniques. © 2023 IEEE.

关键词： Attacks IoT IoT layers machine learning software Defined Networking

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8th international conference on engineering of Computer-Based Systems, ECBS 2023

8th International Conference on Engineering of Computer-Base...

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8th international conference on engineering of Computer-Based Systems, ECBS 2023

ISBN: (纸本)9783031492518

The proceedings contain 26 papers. The special focus in this conference is on engineering of Computer-Based Systems. The topics include: Comparative Analysis of Uppaal SMC, ns-3 and MATLAB/Simulink;Using Automata learning for Compliance Evaluation of Communication Protocols on an NFC Handshake Example;Towards LLM-Based System Migration in Language-Driven engineering;synthesizing Understandable Strategies;ReProInspect: Framework for Reproducible Defect Datasets for Improved AOI of PCBAs;cyber-Physical Ecosystems: Modelling and Verification;integrating IoT Infrastructures in Industrie 4.0 Scenarios with the Asset Administration Shell;A software Package (in progress) that Implements the Hammock-EFL Methodology;toward Responsible Artificial Intelligence Systems: Safety and Trustworthiness;Dynamic Priority Scheduling for Periodic Systems Using ROS 2;continuous Integration of Neural Networks in Autonomous Systems;building a Digital Twin Framework for Dynamic and Robust Distributed Systems;a Simple End-to-End Computer-Aided Detection Pipeline for Trained Deep learning Models;astrocyte-Integrated Dynamic Function Exchange in Spiking Neural Networks;Correct Orchestration of Federated learning Generic Algorithms: Formalisation and Verification in CSP;careProfSys - Combining machine learning and Virtual Reality to Build an Attractive Job Recommender System for Youth: Technical Details and Experimental Data;ambient Temperature Prediction for Embedded Systems Using machine learning;a Federated learning Algorithms Development Paradigm;machine learning Data Suitability and Performance Testing Using Fault Injection Testing Framework;IDPP: Imbalanced Datasets Pipelines in Pyrus;learning in Uppaal for Test Case Generation for Cyber-Physical Systems;a Literature Survey of Assertions in software Testing;FPGA-Based Encryption for Peer-to-Peer Industrial Network Links.

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PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages 20

PTMTorrent: A Dataset for Mining Open-source Pre-trained Mod...

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IEEE/ACM 20th international conference on Mining software Repositories (MSR)

作者： Jiang, Wenxin Synovic, Nicholas Jajal, Purvish Schorlemmer, Taylor R. Tewari, Arav Pareek, Bhavesh Thiruvathukal, George K. Davis, James C. Purdue Univ W Lafayette IN 47907 USA Loyola Univ Chicago Chicago IL USA

ISBN: (纸本)9798350311846

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as "model hubs" support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult - there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data. We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset. The PTMTorrent dataset (v1) is available at: https://***/file-manager?origin_id=55e17a6e-9d8f- 11ed-a2a2-8383522b48d9&origin_path=%2F%7E%2F. Our dataset generation tools are available on GitHub: https://***/10.5281/zenodo.7570357

关键词： Open-Source software Data Mining machine learning Empirical software engineering

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Encoding Techniques for Handling Categorical Data in machine learning-Based software Development Effort Estimation 15

Encoding Techniques for Handling Categorical Data in Machine...

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15th international conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2023

作者： Hosni, Mohamed MOSI Research Team ENSAM University Moulay Ismail of Meknes Meknes Morocco

ISBN: (纸本)9789897586712

Planning, controlling, and monitoring a software project primarily rely on the estimates of the software development effort. These estimates are usually conducted during the early stages of the software life cycle. At this phase, the available information about the software product is categorical in nature, and only a few numerical data points are available. Therefore, building an accurate effort estimator begins with determining how to process the categorical data that characterizes the software project. This paper aims to shed light on the ways in which categorical data can be treated in software development effort estimation (SDEE) datasets through encoding techniques. Four encoders were used in this study, including one-hot encoder, label encoder, count encoder, and target encoder. Four well-known machine learning (ML) estimators and a homogeneous ensemble were utilized. The empirical analysis was conducted using four datasets. The datasets generated by means of the one-hot encoder appeared to be suitable for the ML estimators, as they resulted in more accurate estimation. The ensemble, which combined four variants of the same technique trained using different datasets generated by means of encoder techniques, demonstrated an equal or better performance compared to the single ML estimation technique. The overall results are promising and pave the way for a new approach to handling categorical data in SDEE datasets. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： machine learning

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