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GaussML: An End-to-End In-Database machine learning System 40

GaussML: An End-to-End In-Database Machine Learning System

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40th IEEE international conference on Data engineering, ICDE 2024

作者： Li, Guoliang Sun, Ji Xu, Lijie Li, Shifu Wang, Jiang Nie, Wen Tsinghua University China Huawei Company Switzerland ETH Zürich Switzerland Institute of Software Chinese Academy of Sciences China

ISBN: (纸本)9798350317152

In-database machine learning (In-DB ML) is appealing to database users with security and privacy concerns, as it avoids copying data out of the database to a separate machine learning system. The common way to implement in-DB ML is the ML-as-UDF approach, which utilizes the User-Defined Functions (UDFs) within SQL to implement the ML training and prediction. However, UDFs may introduce security risks with vulnerable code, and suffer from performance problems, as constrained by data access and execution patterns of SQL query operators. To address these limitations, we propose a new in-database machine learning system, namely GaussML, which provides an end-to-end machine-learning ability with native SQL interface. To support ML training/inference within SQL query, GaussML directly integrates typical ML operators into the query engine without UDFs. GaussML also introduces an ML-aware cardinality and cost estimator to optimize the SQL+ML query plan. Moreover, GaussML leverages Single Instruction Multiple Data (SIMD) and data prefetching techniques to accelerate the ML operators for training. We have implemented a series of algorithms inside GaussML in openGauss database. Compared to the state-of-the-art in-DB ML systems like Apache MADlib, our GaussML achieves 2-6× speed-up in extensive experiments. © 2024 IEEE.

关键词： Database systems

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep learning;software Maintenance Prediction Using Stack Ensemble Deep learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar Signal Processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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A Comparative Analysis on the Detection of Web Service Anti-Patterns Using Various Metrics 23

A Comparative Analysis on the Detection of Web Service Anti-...

引用

16th Innovations in software engineering conference, ISEC 2023

作者： Tummalapalli, Sahithi Kumar, Lov Neti, Lalita Bhanu Murthy Krishna, Aneesh Computer Science and Information Systems Bits Pilani Hyderabad India Science and Engineering Curtin University India

ISBN: (纸本)9798400700644

Nowadays, the application of machine learning for developing prediction models is one of the most critical research areas. Early prediction of anti-patterns using machine learning can help developers, and testers fix the design issues and utilize the resources effectively. This work analyzes four different sets of metrics, i.e., source code, WSDL, text, and sequence metrics, to develop web service anti-pattern prediction models. These sets of metrics are treated as an input for models trained using thirty-eight classification techniques to build a model. The experimental finding shows that the models trained using sequence metrics produce better results. The experimental finding also confirmed that the models trained on balanced data achieved better performance than the original data. Further, it is also found that the models trained using CNN and LSTM deep learning approach achieve better results compared to other techniques. © 2023 ACM.

关键词： Web services

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Research on operation optimization problem of energy storage station in microgrid based on improved particle swarm optimization 3

Research on operation optimization problem of energy storage...

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3rd international conference on Signal Processing and machine learning, conf-SPML 2023

作者： Fan, Yikun School of Computer and Software Nanjing University of Information Science and Technology Jiangsu Nanjing210032 China

In many microgrids with a lot of uncontrollable DGs, a well-planned operation of the energy storage station is an important guarantee for the stability and economy of the microgrid. To solve the operation optimization problem, the Chaos mechanism, differential evolution, and random inertia weight were introduced to propose the traditional PSO algorithm. A mathematical model for minimizing the running cost of Microgrid is established, and through MATLAB simulation, both algorithms are used to solve the same problem to observe the improvement of the algorithm. The experimental results show that the IPSO algorithm achieves performance improvement. © Published under licence by IOP Publishing Ltd.

关键词： Particle swarm optimization (PSO)

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A Fabric Defect Detection Algorithm Based on YOLOv8 2

A Fabric Defect Detection Algorithm Based on YOLOv8

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

作者： Zhang, Mingjun Yu, Wenjing Qiu, Hongjun Yin, Jin He, Jinyu Software Engineering Institute of Guangzhou Guangzhou China Guangzhou Civil Aviation College Guangzhou China

ISBN: (纸本)9798350331417

In response to the practical problems faced by manual defect detection in the textile industry, this paper studies an automatic fabric defect detection algorithm based on machine vision. Starting from the research background of deep learning based fabric defect detection algorithms, this article introduces the latest YOLOv8 algorithm model released by Ultratics. Based on the complex and diverse textures of textiles, a coordinate attention mechanism is added to the YOLOv8n model to enhance the network's ability to extract features. The experimental results show that the fabric defect detection algorithm designed in this article has high detection accuracy, mAP up to 79.20%, fast speed, and FPS up to 95.4, which can meet the requirements of online real-time detection with low computational resource requirements and can be deployed on edge intelligent computing devices. The algorithm designed in this article has good comprehensive performance and can promote the deployment of textile defect automatic detection technology in the industry. © 2023 IEEE.

关键词： Algorithm Detection Fabric defect machine vision YOLOv8

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2023 13th international conference on Information Technology in Asia, CITA 2023

2023 13th International Conference on Information Technology...

引用

13th international conference on Information Technology in Asia, CITA 2023

ISBN: (纸本)9798350326208

The proceedings contain 19 papers. The topics discussed include: current trend of software requirement engineering process in IT small and medium enterprises (SMEs) - a systematic literature review;security of cloud-assisted BANs using digital twin;colorectal cancer recognition using deep learning on histopathology images;fog computing in optical access networks: an energy-efficient and deadline-aware task scheduling mechanism;designing effective mathematics learning: augmented reality framework development via the fuzzy Delphi method;a review of semantic-based reasoning framework for context-aware mobile-crowdsourcing;cluster optimization in VANET using MFO algorithm and K-means clustering;a concise review for exploring behaviors to embrace investment robo-advisors;and fingerprinting generation for advanced persistent threats (APT) detection using machine learning techniques.

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Sankalpa - Interactive Digital learning Platform to Enrich Students with Special Needs 5

Sankalpa - Interactive Digital Learning Platform to Enrich S...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Basnayaka, A.I. Epitakaduwa, E.K.G.D. Jayawardena, C.P.U. Zoysa, Ashan Dharmakeerthi, Uditha Jayawardena, Sanath Sri Lanka Institute of Information Technology Dept of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The term special needs mean the special educational needs that the students who suffer from learning disabilities must have due to several reasons. learning impairment is a condition of the neurological system that impairs one's capacity to efficiently acquire and process knowledge. Failures in diagnosing the people with learning disabilities have been repeatedly highlighted and remain unpopular among people in the developing countries like Sir Lanka. Dyscalculia (difficulties in math), dysgraphia (difficulties in writing) and dyslexia (difficulties in reading) can be considered as some of the major learning disability issues. The 'mobile application based' solution 'Sankalpa Math Master' has been created to offer a game based all-in-one solution including screening, and intervention to identify one of the above-mentioned learning disabilities called 'Dyscalculia'. Dyscalculia is generally distinguished by challenges in learning math, including organizing problems, multi-step calculations, transposing numbers, distinguishing directions, and understanding mathematical signs, leading to confusion with basic operations and facts. The provided solution has been accurate with the use of machine learning and deep learning techniques. In the process of screening with the use of Support Vector machine (SVM) machine learning algorithm 99.77% of accuracy level has been obtained. The skill level prediction process has acquired an accuracy level of 97% with the use of Random Forest machine learning algorithm. Sri Lankan traditional education system will be vastly benefited by the high level of accuracy in screening, skill predicting, and other game and voice chat-based activities. The students with special needs will gain more effective and personalized learning experience which will improve their learning abilities and help to close the technology gap in education. © 2023 IEEE.

关键词： Forecasting

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Optimized Energy Distribution in Smart Grid System Using Hybrid machine learning Techniques 8

Optimized Energy Distribution in Smart Grid System Using Hyb...

引用

8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Daorui, Ding Chang, Hong Durga Kumar, Burra Venkata School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350310931

Smart Grid systems are becoming increasingly important with the rapid advent of industrial parks and residential areas. The aim of the Smart Grid system is to modernize and enhance the efficiency, reliability, and flexibility of electrical power grids. It integrates network, smart sensors, and time series prediction machine learning algorithms into the existing grid transmission system to collect the data and make predictions for optimizing decisions. With the help of big data techniques, Smart Grid systems can provide on-demand power supplies. However, most of the proposed prediction algorithms for Smart Grid system highly rely on pre-defined parameters and influential factors, such kind of models fail to dynamically update themselves to cater to the changing world especially when emergencies happen. In this paper, we introduce Dynamic Bayesian Network (DBN) concept and propose a new prediction architecture Smart Grid+ to improve the systems' self-adaptability and resilience to power outages, faults, and disruptions. Because of DBN's ability of dynamical expansion and posterior probability calculation from prior data, our proposed Smart Grid+ responds to emergencies more quickly and predicts the electricity load more precisely. And we believe it can better apply to the requirements of Smart Grid system and has a promising future. © 2023 IEEE.

关键词： Bayesian networks

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A Two-level DDoS attack Detection using Entropy and machine learning in SDN 3

A Two-level DDoS attack Detection using Entropy and Machine ...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Puranik, Kishansmaran Patil, Kirankumar Ghaligi, Guruprasad Jannu, Rajath Patil, Sangamesh Narayan, D.G. Kachavimath, Amit KLE Technological University School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350338607

software Defined Networking (SDN) separates the control plane from the data plane and has evolved into the next generation of internet architecture because of programmability and flexibility. This enables the centralized controller to completely control the entire network. Since the control plane is centralized, it is more prone to attacks. Distributed Denial of Service (DDoS) attack is one of the primary SDN attacks. In DDoS, a malicious attacker attempts to interrupt services by overwhelming the target network with malicious internet traffic. The resources of the impacted server, such as bandwidth and buffer size, are reduced that leads to the unavailability of resources to legitimate clients. In this work, we propose a new two-level approach for the detection of DDoS attacks using a faster entropy method on flow-based analysis and machine learning techniques for classification. Initially, entropy method is used to identify the malicious nodes. Later, the machine learning algorithms like Naïve Bayes, support vector machine, and random forest are used to detect attacks. We use Mininet emulator with POX controller to implement the proposed work. The results reveal that random forest has a better accuracy of 98.3% compared to the remaining algorithms. Furthermore, the proposed two-level approach reduces the false positive rate. © 2023 IEEE.

关键词： Network security

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Using Artificial Intelligence to Defend Internet of Things for Smart City Networks 1

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8th international conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2023

作者： Nunn, Aaron Prasad, P.W.C. Charles Sturt University Wagga WaggaNSW2650 Australia

ISBN: (数字)9783031717734

ISBN: (纸本)9783031717727

As these IoT devices have been embedded and operate within Smart City networks they are also open to vulnerabilities similar to other technologies, however, unlike other technologies, these IoT cannot run a defence in depth protective layer as these devices will run very specific niche software from vendors for their intended purpose. This leaves many of these IoT devices, which can operate power grids, control transit across the cityscape, and provide communications to its citizens or surveillance for protection, are at significant risk of exposure with the emergence of using Artificial Intelligence to enhance attack techniques and therefore would be predisposed to cyber-attacks. This research project explores concepts and proposes a framework for using Artificial Intelligence in combination with modern cyber defence strategies, the application of machine learning or Deep learning to existing technical controls, such as an Intrusion Detection System, then using a software-Defined Networking or Fog Computing as the delivery medium to overcome the resource limitations for a Smart City IoT network has significant potential. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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