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Guarding the Grid: data Poisoning Attacks and Countermeasures in Power System Stability Assessment AI Model

Guarding the Grid: Data Poisoning Attacks and Countermeasure...

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Electrical Engineering and control Science (IC2ECS), International conference on

作者： Zhenyu Tang Qingjun Xiao School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331510107

ISBN: (纸本)9798331510114

Artificial Intelligence (AI) algorithms have become a critical tool for addressing the transient stability assessment issue in modern power systems. By leveraging historical or simulation data to establish correlations between features and transient stability, AI eliminates the need for modeling and analyzing intricate physical mechanisms, thereby reducing problem complexity and enhancing computational efficiency. However, the black-box nature of AI introduces significant security risks. Malicious attackers can exploit vulnerabilities during the training process, embedding backdoors in the models to manipulate their outputs and potentially disrupt power system operations. This paper explores the feasibility of embedding backdoors in AI models for power systems transient stability classifier and proposes a data poisoning-based backdoor attack method tailored to these systems. A backdoor trigger is designed to exploit the difficulty of accessing system nodes, causing AI models to misclassify specific scenario samples. To counter such threats, detection schemes are developed at both the model and sample levels. Finally, the effectiveness of the proposed attack and detection methods is validated through a case study on a AI-driven transient stability classifier.

关键词： Training Analytical models Computational modeling Power system stability Stability analysis data models Threat assessment Security Artificial intelligence Transient analysis

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Application of Particle Swarm Optimization Algorithm in Statistical analysis of Financial Sharing System

Application of Particle Swarm Optimization Algorithm in Stat...

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2023 Asia-Europe conference on Electronics, data processing and Informatics, ACEDPI 2023

作者： Liu, Ran Kunming University Planning and Financial Affairs Department Kunming China

ISBN: (纸本)9798350301151

As the scale of larger enterprises continues to expand, the financial management work of various subsidiaries has also increased, which has brought certain benefits to the company, but there have also been some problems. These problems have had a great impact on the company's development and development. The paper first designs the structure of the shared financial system, analyzes its business requirements, and puts forward corresponding functional requirements on this basis. In view of the needs of the system, the thesis focuses on the design and implementation of various subsystems such as financial management, quality management, and work subsystems. Through analysis, it is concluded that organizational structure reform is the main financial management framework, financial management control is the system guarantee, PSO-based financial cloud technology support is the implementation tool, and financial business process optimization is the principle, and performance appraisal is used as a way of reward and punishment, and a financial sharing system is gradually established. © 2023 IEEE.

关键词： Quality management

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14th International conference on Complex Systems Design and Management, CSD&M 2023

14th International Conference on Complex Systems Design and ...

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14th International conference on Complex Systems Design and Management, CSD and M 2023

ISBN: (纸本)9789819965106

The proceedings contain 34 papers. The special focus in this conference is on International conference on Complex Systems Design and Management. The topics include: Model Compression Method Based on Knowledge Distilling and Adversarial Learning;research on Hardware-in-the-Loop Simulation for Aircraft Electric Power System;an Assumption of R&D Method Driven by Model and data;research on the Model-Based process and Method for Aviation Equipment Requirement Demonstration;enterprise modeling for Architecture-Centric Production Systems Planning;model-Based Embedded Radar System Software Development and Verification;model-Based Design Method and Practice of Avionics System Architecture in Civil Aircraft;applying Systems Thinking and Architectural Thinking to Improve Model-Based Systems Engineering Practice: Concepts and Methodology;Top-Down Military System-of-Systems Design Using MBSE Based on UAF: A Case Study;a Generalized Reuse Framework for Systems Engineering;PRODEC-Based Task analysis for the Design of Semi-Automated Trains;Risk Assessment Method of Aircraft Engine Product Supply Chain Based on AHP analysis;Design of Ground Integrated Testing Equipment Based on MBSE;model Based analysis and Verification Method for Helicopter System Performance Requirements;A SysML-Based Architecture Framework for Helicopter;Design and modeling of Nuclear Power Inspection Robot Based on MBSE;A Novel MBSE-Based Design Method for Search and Rescue Humanoid Robots;Design Method of Task Meta-model of Avionics System Architecture Based on DM2;investigation of a Model-Based Approach to a Grid Fin System Design;architecture Design of Model-Based Land Combat Equipment System;the Research on the Task Scheduling and Optimization Technology for Flight Tests;research and Application of Model-Based Aircraft Complex Function analysis Method.

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data Driven Retail Price Optimization using XGBoost and Predictive modeling

Data Driven Retail Price Optimization using XGBoost and Pred...

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2025 International conference on Intelligent Computing and control Systems, ICICCS 2025

作者： Daruvuri, Rajesh Patibandla, Kiran Kumar Mannem, Pravallika University of The Cumberlands United States

ISBN: (纸本)9798331512088

Retail price optimization is a critical challenge in contemporary commerce, requiring robust methodologies to balance profitability and market competitiveness. This study introduces a data-driven framework employing advanced machine learning models, particularly XGBoost, to predict sales quantities, assess price elasticity, and determine optimal pricing strategies for diverse product categories. By leveraging historical sales data, seasonality indices, and category-specific attributes, the proposed approach effectively captures complex non-linear relationships that traditional models fail to address. Comprehensive experiments reveal XGBoost's superior performance in comparison to linear regression and other baseline methods, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) across multiple datasets. Furthermore, optimal price simulations indicate significant opportunities for revenue enhancement through targeted pricing adjustments. Visual and tabular analyses underscore the importance of seasonality and category-specific dynamics in shaping sales trends and pricing strategies. The findings validate the efficacy of the proposed framework in addressing the multifaceted demands of retail price optimization, paving the way for more informed and profitable decision-making in retail operations. © 2025 IEEE.

关键词： machine learning price elasticity Retail price optimization sales forecasting seasonality analysis XGBoost

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Neural Point process for Learning Spatiotemporal Event Dynamics 4

Neural Point Process for Learning Spatiotemporal Event Dynam...

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4th Annual conference on Learning for Dynamics and control (L4DC)

作者： Zhou, Zihao Yang, Xingyi Rossi, Ryan A. Zhao, Handong Yu, Rose Univ Calif San Diego La Jolla CA 92093 USA Natl Univ Singapore Singapore Singapore Adobe Res San Jose CA USA

Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However, most existing methods only consider temporal dynamics without spatial modeling. We propose Deep Spatiotemporal Point process (DeepSTPP), a deep dynamics model that integrates spatiotemporal point processes. Our method is flexible, efficient, and can accurately forecast irregularly sampled events over space and time. The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process. The intensity function enjoys closed form integration for the density. The latent process captures the uncertainty of the event sequence. We use amortized variational inference to infer the latent process with deep networks. Using synthetic datasets, we validate our model can accurately learn the true intensity function. On real-world benchmark datasets, our model demonstrates superior performance over state-of-the-art baselines. Our code and data can be found at the link.

关键词： spatiotemporal dynamics neural point processes kernel density estimation

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Reproducibility of LLM-based Recommender Systems: the Case Study of P5 Paradigm 24

Reproducibility of LLM-based Recommender Systems: the Case S...

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18h ACM conference on Recommender Systems (RecSys)

作者： Lops, Pasquale Silletti, Antonio Polignano, Marco Musto, Cataldo Semeraro, Giovanni Univ Bari Aldo Moro Bari Italy

ISBN: (纸本)9798400705052

Recommender systems can significantly benefit from the availability of pre-trained large language models (LLMs), which can serve as a basic mechanism for generating recommendations based on detailed user and item data, such as text descriptions, user reviews, and metadata. On the one hand, this new generation of LLM-based recommender systems paves the way for dealing with traditional limitations, such as cold-start and data sparsity. Still, on the other hand, this poses fundamental challenges for their accountability. Reproducing experiments in the new context of LLM-based recommender systems is challenging for several reasons. New approaches are published at an unprecedented pace, which makes difficult to have a clear picture of the main protocols and good practices in the experimental evaluation. Moreover, the lack of proper frameworks for LLM-based recommendation development and evaluation makes the process of benchmarking models complex and uncertain. In this work, we discuss the main issues encountered when trying to reproduce P5 (Pretrain, Personalized Prompt, and Prediction Paradigm), one of the first works unifying different recommendation tasks in a shared language modeling and natural language generation framework. Starting from this study, we have developed LaikaLLM, a framework for training and evaluating LLMs, specifically for the recommendation task. It has been used to perform several experiments to assess the impact of using different LLMs, different personalization strategies, and a novel set of more informative prompts on the overall performance of recommendations in a fully reproducible environment.

关键词： Recommender Systems Reproducibility analysis Large Language Models LLMs Recommendation Frameworks

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Detecting and Measuring Aggressive Location Harvesting in Mobile Apps via data-flow Path Embedding 23

Detecting and Measuring Aggressive Location Harvesting in Mo...

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2023 ACM SIGMETRICS International conference on Measurement and modeling of Computer Systems, SIGMETRICS 2023

作者： Lu, Haoran Zhao, Qingchuan Chen, Yongliang Liao, Xiaojing Lin, Zhiqiang Indiana University Bloomington BloomingtonIN United States City University of Hong Kong Hong Kong Hong Kong The Ohio State University ColumbusOH United States

ISBN: (纸本)9798400700743

Today, location-based services have become prevalent in the mobile platform, where mobile apps provide specific services to a user based on his or her location. Unfortunately, mobile apps can aggressively harvest location data with much higher accuracy and frequency than they need because the coarse-grained access control mechanism currently implemented in mobile operating systems (e.g., Android) cannot regulate such behavior. This unnecessary data collection violates the data minimization policy, yet no previous studies have investigated privacy violations from this perspective, and existing techniques are insufficient to address this violation. To fill this knowledge gap, we take the first step toward detecting and measuring this privacy risk in mobile apps at scale. Particularly, we annotate and release the first dataset to characterize those aggressive location harvesting apps and understand the challenges of automatic detection and classification. Next, we present a novel system, LocationScope, to address these challenges by (i) uncovering how an app collects locations and how to use such data through a fine-Tuned value set analysis technique, (ii) recognizing the fine-grained location-based services an app provides via embedding data-flow paths, which is a combination of program analysis and machine learning techniques, extracted from its location data usages, and (iii) identifying aggressive apps with an outlier detection technique achieving a precision of 97% in aggressive app detection. Our technique has further been applied to millions of free Android apps from Google Play as of 2019 and 2021. Highlights of our measurements on detected aggressive apps include their growing trend from 2019 to 2021 and the app generators' significant contribution of aggressive location harvesting apps. © 2023 Owner/Author.

关键词： Location

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Computer Model of the Building Envelope the Thermophysical Characteristics Determining process 3

Computer Model of the Building Envelope the Thermophysical C...

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3rd International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023

作者： Babak, Vitalii Dekusha, Oleg Kovtun, Svitlana Shcherbak, Leonid Kobzar, Sergiy General Energy Institute of National Academy of Sciences of Ukraine 172 Antonovich str Kyiv03150 Ukraine Institute of Engineering Thermophysics of NAS of Ukraine 2a Marii Kapnist str. Kyiv03057 Ukraine

Thermophysical properties are a key factor in controlling heat losses and as a result in assessing the building's energy efficiency. For control heat losses one of the main thermophysical characteristics is the specific thermal resistance of the building envelope, which is defined as the resistance of individual characteristic zones of the envelope taking into account their area. Various calculation methods, physical and mathematical models, computational fluid dynamics (CFD) modeling and experimental studies are used to determine thermal resistance. This work combines all the above-mentioned approaches to control the thermophysical characteristics of the building. In the work, a computer simulation of the process of complex radiative and convective-conductive heat exchange during the control of the thermophysical characteristics of the building envelope was carried out, taking into account the locations of the sensors and the influence of their parameters on the control result. Validation of the CFD model was performed using experimental data and shown good convergence of the obtained results. In work also shown practical application of the CFD model for the analysis of the sensor’s parameters influence on the thermophysical characteristics control of the building envelope. © 2023 CEUR-WS. All rights reserved.

关键词： Heat resistance

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Smart Approaches for Detecting and Quantifying Stiction of control Valve Using Signal processing and Machine Learning Techniques 50

Smart Approaches for Detecting and Quantifying Stiction of C...

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50th International conference on Computers and Industrial Engineering: Sustainable Digital Transformation, CIE 2023

作者： Ferik, Sami El Al-Nasser, Mustafa Al-Amoudi, Abrar Al-Najim, Abdullatif Research Center for Smart Mobility and Logistics Control and Instrumentation Engineering Department King Fahd Univesity of Petroleum and Minerals Dhahran31261 Saudi Arabia R and D Department Yokogawa Saudi Arabia Company Dhahran Techno-Valley Dhahran31952 Saudi Arabia

ISBN: (纸本)9781713886952

Variations in process variables is one of the major concern in industrial plants. This varation can have a significant impact on both the quality of the product and the performance of equipment, resulting in increased operating and maintenance costs as well as potential safety issues. One of the common sources of process variation is control valve stiction. Therefore, early detection and quantification of valve stiction is crucial for maintaining product quality and reducing maintenance costs. Diagnosing stiction in a smart valve equipped with a positioner is a relatively simple process. However, for an ordinary control valve, the diagnostic process is not straightforward due to absence of control valve output. As there are large number of ordinary control installed in plants, our focus in this research on developing smart algorithms for detecting and quantifying stiction for this specific type of control valves. The model estimates control valve output (MV) using an inverse model and smart search methods. In order to implement the proposed approach, we used state-of-the-art signal processing and machine learning techniques to develop a trustworthy strategy for stiction analysis for ordinary control valve which is applicable also to variable operating setups case. Testing on publicly available industrial data showed that the proposed approach functioned well for all tested scenarios to detect the stiction and provided a good estimate of the stiction value compared to other avalible techniques. © 2023 Computers and Industrial Engineering. All rights reserved.

关键词： Signal processing

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Advancements in Spasticity Management: A Design Framework with PID Integration for Upper Limb Spasticity Training Device (ULSTraD) 2

Advancements in Spasticity Management: A Design Framework wi...

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2nd International conference on Mechatronics, control and Robotics, ICMCR 2024

作者： Othman, Nurul Atiqah Zakaria, Noor Ayuni Che Low, Cheng Yee Hashim, Natiara Mohamad Johar, Khairunnisa Zohadi, Muhammad Afiq Mohd Yee, Jingye Hanapiah, Fazah Akhtar Universiti Teknologi Mara School of Mechanical Engineering College of Engineering Selangor Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Mechanical and Manufacturing Engineering Johor Malaysia Universiti Teknologi Mara Sungai Buloh Faculty of Medicine Selangor Malaysia Fraunhofer Iem Corporate Innovation Paderborn33102 Germany

ISBN: (纸本)9798350396096

The upper limb spasticity training device (ULSTraD) is a simulator designed to replicate various spasticity behaviors observed in patients with upper limb spasticity. Through a systematic development process involving requirements analysis, clinical data collection, conceptual design, prototyping, and integration of a Proportional-Integral-Derivative (PID) controller, the ULSTraD achieves precise control over servo motors. The PID controller, in conjunction with the PWM controller PCA9885 and real-time feedback from the MPU6050 angle sensor, enables accurate spasticity behavior emulation. Optimal PID gains (Kp = 0.9, Ki = 1.0, and Kd = 0.00) were determined through tuning, resulting in the desired target position. By incorporating FSR 460 and conducting tests at various angles using the PID controller, ULSTraD displayed real-time responsiveness to external forces, enhancing its stability and accurately emulating upper limb spasticity behaviors. Guided by anthropometric measurements and clinical data, the ULSTraD closely resembles a real human arm affected by spasticity. The prototyping process involved 3D printing, welding, and assembly, leading to a functional prototype refined through iterative testing. To assess the similarity in range of motion (ROM) between ULSTraD and the human arm, a Paired Sample T-test was conducted, comparing the flexion and extension movements. The obtained p-value from the test was 0.9087, which indicates that there is no statistically significant difference in ROM between ULSTraD and the human arm during both flexion and extension movements. This validates ULSTraD's ability to replicate the human arm's range of motion affected by spasticity. © 2024 IEEE.

关键词： Three term control systems

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