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Predicting Fairness of ML software Configurations 20

Predicting Fairness of ML Software Configurations

引用

20th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Robles Herrera, Salvador Monjezi, Verya Kreinovich, Vladik Trivedi, Ashutosh Tizpaz-Niari, Saeid Univ Texas El Paso El Paso TX 79968 USA Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9798400706752

this paper investigates the relationships between hyperparameters of machine learning and fairness. Data-driven solutions are increasingly used in critical socio-technical applications where ensuring fairness is important. Rather than explicitly encoding decision logic via control and data structures, the ML developers provide input data, perform some pre-processing, choose ML algorithms, and tune hyperparameters (HPs) to infer a program that encodes the decision logic. Prior works report that the selection of HPs can significantly influence fairness. However, tuning HPs to find an ideal trade-off between accuracy, precision, and fairness has remained an expensive and tedious task. Can we predict the fairness of HP configuration for a given dataset? Are the predictions robust to distribution shifts? We focus on group fairness notions and investigate the HP space of 5 training algorithms. We first find that tree regressors and XG-Boots significantly outperformed deep neural networks and support vector machines in accurately predicting the fairness of HPs. When predicting the fairness of ML hyperparameters under temporal distribution shift, the tree regressors outperform the other algorithms with reasonable accuracy. However, the precision depends on the ML training algorithm, dataset, and protected attributes. For example, the tree regressor model was robust for training data shift from 2014 to 2018 on logistic regression and discriminant analysis HPs with sex as the protected attribute;but not for race and other training algorithms. Our method provides a sound framework to efficiently perform fine-tuning of ML training algorithms and understand the relationships between HPs and fairness.

关键词： ML Hyperparameters Fairness distribution shifts

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Crafting Tomorrow's Trends with Automated Precision, Efficiency, and Faultless Excellence in Apparel Industry for Medium Scale Businesses 6

Crafting Tomorrow's Trends with Automated Precision, Efficie...

引用

6th international conference on Advancements in Computing, ICAC 2024

作者： Hiruni Herath, H.M. Jayaweera, J.M.M.G. Jayasinghe, J.M.Y. Randeni, K.M.T.D. Amarasena, Nelum Chathuranga Rathnayake, Samadhi Chandanathilaka, S.N. Sri Lankan Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lankan Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

In the evolving landscape of Sri Lanka's apparel industry, the predominance of manual methods in the pre-production phase necessitates innovative technological interventions to enhance efficiency. this research explores the complexities of garment production, addressing critical challenges such as machine breakdowns, maintenance, and workforce management. By employing advanced technologies such as image recognition, loT devices, and machine learning, this study aims to transform pre-production decision-making, optimize sewing machine performance, implement predictive maintenance strategies, and refine workforce optimization. these initiatives, driven by domain expertise and cutting-edge methodologies, not only aim to bridge existing gaps in the industry but also pave the way for significant advancements, ensuring a future characterized by precision, efficiency, and operational excellence. © 2024 IEEE.

关键词： predictive maintenance

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Enhancing Power Grid Load Forecasting for Ho Chi Minh City Using a Multiplayer Algorithm and Long-Short Term Memory Fusion 6

Enhancing Power Grid Load Forecasting for Ho Chi Minh City U...

引用

6th international conference on Power, Energy and Innovations, ICPEI 2024

作者： Nguyen, Khai Phuc Nguyen, Anh Lan Ngoc Buu Lam, Qui Ho Chi Minh City University of Technology Vietnam National University Ho Chi Minh City Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350356779

this paper presents an advanced power grid load forecasting approach designed specifically for Ho Chi Minh City. Our method combines a multiplayer algorithm with Long Short-Term Memory networks to improve the accuracy and efficiency of load prediction. the use of a multiplayer algorithm allows multiple models to learn collaboratively, providing a better understanding of complex load patterns. Long Short-Term Memory networks, which are proficient at capturing long-term dependencies, enhance predictive capabilities by considering historical load data. the model is trained on historical power consumption data from Ho Chi Minh City and is compared against traditional forecasting methods to evaluate its performance. the results show a significant improvement in forecasting accuracy, demonstrating the potential of the proposed approach to optimize power grid management in urban environments like Ho Chi Minh City. © 2024 IEEE.

关键词： Power grids

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Machine Learning for Myocardial Infarction complication: A Metrics-Based Investigation of Different models 6

Machine Learning for Myocardial Infarction complication: A M...

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： thomas Abraham, J.V. Sriram, R. Shashaank, V. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

Myocardial infarction, known to be heart attack, is still a major global health concern. Recognizing and categorizing MI complications in a timely manner is crucial for effective patient care and better outcomes. In this study, we present an innovative approach that harness power of machine learning to classify complications associated with myocardial infarction using a wide range of clinical data. Our study draws from an extensive dataset that encompasses patient records, imaging data, and lab results. An integral component of our approach involves employing feature selection techniques to pinpoint the most relevant predictors from the vast pool of clinical information. this not only makes the model more understandable but also enhances its predictive accuracy by curbing overfitting and computational complexities. We make use of advanced ML algorithms such as random forests, support vector machines, and deep neural networks, to delve into the intricate relationships hidden within the data. © 2023 IEEE.

关键词： Support vector machines

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A Qualitative Study on Using ChatGPT for software Security: Perception vs. Practicality 6

A Qualitative Study on Using ChatGPT for Software Security: ...

引用

6th IEEE international conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Mehdi Kholoosi, M. Ali Babar, M. Croft, Roland The University of Adelaide CREST - the Centre for Research on Engineering Software Technologies Adelaide Australia Cyber Security Cooperative Research Centre Australia

ISBN: (纸本)9798350386745

Artificial Intelligence (AI) advancements have enabled the development of Large Language models (LLMs) that can perform a variety of tasks with remarkable semantic understanding and accuracy. ChatGPT is one such LLM that has gained significant attention due to its impressive capabilities for assisting in various knowledge-intensive tasks. Due to the knowledge-intensive nature of engineering secure software, Chat-GPT's assistance is expected to be explored for security-related tasks during the development/evolution of software. To gain an understanding of the potential of ChatGPT as an emerging technology for supporting software security, we adopted a twofold approach. Initially, we performed an empirical study to analyse the perceptions of those who had explored the use of ChatGPT for security tasks and shared their views on Twitter. It was determined that security practitioners view ChatGPT as beneficial for various software security tasks, including vulnerability detection, information retrieval, and penetration testing. Secondly, we designed an experiment aimed at investigating the practicality of this technology when deployed as an oracle in real-world settings. In particular, we focused on vulnerability detection and qualitatively examined ChatGPT outputs for given prompts within this prominent software security task. Based on our analysis, responses from ChatGPT in this task are largely filled with generic security information and may not be appropriate for industry use. To prevent data leakage, we performed this analysis on a vulnerability dataset compiled after the OpenAI data cut-off date from real-world projects covering 40 distinct vulnerability types and 12 programming languages. We assert that the findings from this study would contribute to future research aimed at developing and evaluating LLMs dedicated to software security. © 2024 IEEE.

关键词： Information leakage

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Capturing wave energy converter’s extreme response: Experimental validation of DualSPHysics and STAR CCM+ 6th

Capturing wave energy converter’s extreme response: Experim...

引用

6th international conference on Renewable Energies Offshore, RENEW 2024

作者： Dell’Edera, O. Niosi, F. Capasso, S. Martínez-Estévez, I. Tagliafierro, B. DIMEAS Dipartimento di Ingegneria Meccanica e Aerospaziale Politecnico di Torino Torino Italy University of Salerno Italy Environmental Physics Laboratory CIM-UVIGO Universidade de Vigo Ourense Spain Maritime Engineering laboratory Universitat Politècnica de Catalunya – BarcelonaTech Barcelona Spain School of Natural and Built Environment Queen’s University Belfast Belfast United Kingdom

ISBN: (纸本)9781032905570

Validating numerical models against their physical realizations is an integral aspect, very essential for their development and a reliable assessment of their accuracy. this study focuses on comparing two different approaches: the open-source DualSPHysics, and the commercial software STAR CCM+, which have emerged as high-fidelity tools in evaluating wave energy converters’ (WEC) performance. Notably, they belong to opposite ends of the modelling spectrum, with the former relying on a Lagrangian approach, and the latter employing a Eulerian one. As ground through, our validation is performed using an experimental investigation of the Pendulum Wave Energy Converter (PeWEC), focusing on its kinematic and mooring forces. In addition to rigours validations, this comparative analysis provides an overview of the advantages and short-comings of each employed numerical model, discussing pre-processing time, computational performance, and overall code platform flexibility for end-users. the aim of this comprehensive assessment is to under-line and compare performance and accuracy of these models when employed to capture the extreme response of resonant devices, such as WECs. © 2025 the Author(s).

关键词： Stars

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Multi-Index Decision Analysis Method Based on TOPSIS and Random Forest Model 6

Multi-Index Decision Analysis Method Based on TOPSIS and Ran...

引用

6th international conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Huo, Hongying School of Information Science Beijing Language and Culture University Beijing100083 China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353174

this study introduces a comprehensive multi-index decision analysis framework integrating TOPSIS and the Random Forest model, focusing on various insurance-related models. the research primarily develops and tests three models: the InsuraClient Ecosystem Connectivity Model (ICEC), the InsuraSite Selection Analytics Model (ISSA), and the Community Building Preservation Guideline Model (CBPG). Each model is equipped to analyze different aspects of the insurance industry through statistical methods such as GM (11), ARMA, and ARIMA models to predict risk awareness, claims, and the frequency of natural disasters, respectively. the ICEC model applies multiple linear regression to forecast profits and guide insurance decisions based on client risk awareness and natural disaster data. In contrast, the ISSA model uses TOPSIS to optimize real estate development locations by considering factors like infrastructure and population growth. Lastly, the CBPG model utilizes factor analysis and Random Forest to preserve community buildings by evaluating demographic, and construction-related factors. Overall, the paper extends traditional decision analysis models by effectively combining these methodologies to tackle analytical challenges, offering new methodological advancements in data-driven environments. © 2024 IEEE.

关键词： Deep learning Analytical models Statistical analysis Disasters Insurance predictive models Prediction algorithms

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BOAformer: Object Detection Network Based on Background-Object Attention Transformer 6

BOAformer: Object Detection Network Based on Background-Obje...

引用

6th international conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Yang, Yong Liang, Chenbin Huang, Shuying Wang, Xiaozheng School of Computer Science and Technology Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

ISBN: (纸本)9798350353174

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many prediction boxes and use the same prediction operation on them. Although these methods can obtain higher predictive values for high certainty objects, they may identify objects with lower certainty, such as small or fuzzy objects, as negative samples or miss them. To address this issue, this paper presents an object detection network based on background-object attention transformer (BOAformer), which improves the accuracy of object detection by establishing the relationship between background and object features. Firstly, the existing classification model is employed to obtain scores of object prediction boxes. On this basis, a scoring mechanism is designed to filter out high scoring object prediction boxes to reduce the computational complexity of the later network. then, to improve the accuracy of object detection, BOAformer is constructed, which obtains auxiliary classification values of objects by learning the relationship between background and object features, and adds them to the initial detection results. Finally, many experiments are conducted to verify the effectiveness of BOAformer. the experimental results show that the designed network improves the accuracy of object detection and can detect small or lost objects in the background. © 2024 IEEE.

关键词： Prediction models

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Edge Intelligence Hybrid Framework for Real-Time IoT Applications using Multi-Stage Adaptive Processing Methods 6

Edge Intelligence Hybrid Framework for Real-Time IoT Applica...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Suneel, Sajja Yadav, Rakesh Kumar Nirmala Devi, K. Teja sree, B. Aarthi, R. Upadhyay, Shrikant Institute of Aeronautical Engineering Dundigal Telangana Hyderabad500043 India Amity School of Engineering and Technology Amity University Bihar Patna India Department of Computer Applications Madanapalle Institute of Technology & Science India Department of IT S.R.K.R Engineering College Bhimavaram India Department of ECE Rajalakshmi Institute of Technology Kuthambakkam Post Tamil Nadu Chembarambakkam600124 India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

the rapid proliferation of IoT devices has created an unprecedented need for real-time data processing and decision-making. this paper proposes an Edge Intelligence Hybrid Framework designed for real-time IoT applications using multi-stage adaptive processing methods. the framework structured into three layers: edge-level processing, fog-level aggregation, and cloud-level computation, enabling efficient distribution of tasks across the hierarchy. At the edge, lightweight models and preprocessing techniques, such as Kalman Filtering and Huffman Coding, ensure low-latency and energy-efficient operation. Intermediate computations and model synchronization occur at the fog level, while high-complexity tasks like deep learning model training performed at the cloud level. Results demonstrate that edge-level processing achieves an accuracy of 85.4%, while fog and cloud levels improve accuracy to 89.4% and 94.9%, respectively. Latency at the edge is as low as 9–13ms, compared to fog (23–29ms) and cloud (117–125ms), ensuring suitability for time-critical IoT tasks. the proposed framework effectively balances latency, accuracy, and resource optimization, making it ideal for applications such as predictive maintenance, healthcare, and smart surveillance. © 2025 IEEE.

关键词： Kalman filters

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Application of ground 3D laser scanning technology in the quick construction of 3D models of reservoir hubs 6

Application of ground 3D laser scanning technology in the qu...

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6th international conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Li, Guojing Zhang, Liang Du, Yi Shandong Water Conservancy Survey and Design Institute Co. Ltd Jinan China

ISBN: (纸本)9781510688018

At present, there are fewer studies on the fusion of ground 3D laser scanning technology and optical image for 3D modeling. To address this issue, this paper takes the rapid construction of the 3D model of the reservoir hub as the research object, combines the high-precision features of the ground 3D laser point cloud and the advantages of the rich texture information of the optical image, fuses the two kinds of heterogeneous data, and builds a 3D model of the reservoir hub quickly and efficiently, and develops a number of 3D model application functions by using the software development tools according to the actual application requirements. A number of 3D modeling application functions are developed using software development tools according to practical application requirements. Meanwhile, an improved target point cloud splicing method is proposed and successfully utilized in the case study. this study provides an important reference for the application of 3D laser scanning technology in the rapid construction of 3D models. © 2025 SPIE.

关键词： Laser applications

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