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A smart data holistic approach for context-aware data analytics (AETHER-UA) 42

A smart data holistic approach for context-aware data analyt...

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42nd International conference on Conceptual Modeling: ER Forum, 7th Symposium on Conceptual Modeling Education, SCME 2023, Project Exhibitions, Posters and Demos, and Doctoral Consortium, ER-Companion 2023

作者： Lavalle, Ana Maté, Alejandro Trujillo, Juan Teruel, Miguel A. Díaz, Alexander Sánchez Lucentia Research Group Department of Software and Computing Systems University of Alicante Carretera de San Vicent del Raspeig s/n San Vicent del Raspeig03690 Spain

A smart data holistic approach for context-aware data analytics (AETHER-UA) is one of the four sub-projects, developed in the University of Alicante, being part of the whole project AETHER. This project is being developed by four partners: (i) University of Malaga - Coordinator;(ii) University of Alicante, (iii) University of Castilla La-Mancha, and (iv) University of Seville. The project is funded by the Ministry of Science and Innovation. The main goal of this project is to advance towards a knowledge-based framework integrating novel solutions for data, process and business analytics. The research activities for designing and developing Aether will mainly focus on three main challenges: the characterization of the datasets, the improvement and automation of the algorithms, and the generation of mechanisms to enhance model explainability and interpretation of the results. The project is highly related to data processing, integration, analysis and modeling. More concretely, within the AETHER-UA project, several proposals are being developed for the modeling of user’s requirements for machine learning applications, the developing of a framework based on Model Driven Development (MDD) for eXplanable artificial intelligence and several approaches for the data bias analysis. © 2023 CEUR-WS. All rights reserved.

关键词： Conceptual Modeling Data Bias Data Integration Modeling of machine learning applications Smart Data User’s requirements

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Estimating Identifiable Causal Effects through Double machine learning 35

Estimating Identifiable Causal Effects through Double Machin...

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35th AAAI conference on artificial intelligence / 33rd conference on Innovative applications of artificial intelligence / 11th Symposium on Educational Advances in artificial intelligence

作者： Jung, Yonghan Tian, Jin Bareinboim, Elias Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Iowa State Univ Dept Comp Sci Ames IA USA Columbia Univ Dept Comp Sci New York NY 10027 USA

ISBN: (纸本)9781577358664

Identifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal knowledge about the underlying system. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. Recently, a method known as double/debiased machine learning (DML) (Chernozhukov et al. 2018) has been proposed to learn parameters leveraging modern machine learning techniques, which is both robust to model misspecification and bias-reducing. Still, DML has only been used for causal estimation in settings when the back-door condition (also known as conditional ignorability) holds. In this paper, we develop a new, general class of estimators for any identifiable causal functionals that exhibit DML properties, which we name DML-ID. In particular, we introduce a complete identification algorithm that returns an influence function (IF) for any identifiable causal functional. We then construct the DML estimator based on the derived IF. We show that DML-ID estimators hold the key properties of debiasedness and doubly robustness. Simulation results corroborate with the theory.

关键词： machine learning

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Automated Fraud Detection in Financial Transactions using machine learning: An Ensemble Perspective

Automated Fraud Detection in Financial Transactions using Ma...

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artificial intelligence and machine learning applications Theme: Healthcare and Internet of Things (AIMLA), International conference on

作者： Tamanna Shivani Kamboj Lovedeep Singh Tanvir Kaur University Institute of Sciences Chandigarh University Mohali India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Financial fraud is an increasing concern for both organizations and consumers as digital financial transactions become more prevalent. This presentation offers a comprehensive introduction to the computerized identification of fraudulent activity within financial transactions, examining the methods and technological advancements utilized to prevent such actions. The research explores the evolution of fraud detection systems, demonstrating how advanced automated frameworks leveraging machine learning (ML) and artificial intelligence (AI) algorithms including XGB and Random Forest have superseded more rudimentary human review processes. It illustrates the critical role data analytics performs in evaluating immense volumes of transactional data to identify potential markers of fraud. Additionally, it emphasizes the importance of collaboration between financial institutions, regulatory bodies, and information technology companies to develop robust and efficacious fraud defense systems. The study also focuses on the advantages automated fraud identification solutions hold over more conventional approaches, including improved accuracy, efficiency, and cost-effectiveness. The ongoing advancement of the automated fraud detection field is highlighted as vital, with technologies such as explainable AI simplifying the comprehension of decision-making workflows and promoting transparent communication between stakeholders.

关键词： machine learning algorithms Reviews Explainable AI Finance Medical services Fraud Stakeholders

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DiffuseBot: Breeding Soft RobotsWith Physics-Augmented Generative Diffusion Models 37

DiffuseBot: Breeding Soft RobotsWith Physics-Augmented Gener...

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37th conference on Neural Information Processing Systems (NeurIPS)

作者： Wang, Tsun-Hsuan Zheng, Juntian Ma, Pingchuan Du, Yilun Kim, Byungchul Spielberg, Andrew Tenenbaum, Joshua B. Gan, Chuang Rus, Daniela MIT Cambridge MA 02139 USA Tsinghua Univ Beijing Peoples R China MIT IBM Watson AI Lab Cambridge MA USA Harvard Cambridge MA USA UMass Amherst Amherst MA USA

ISBN: (纸本)9781713899921

Nature evolves creatures with a high complexity of morphological and behavioral intelligence, meanwhile computational methods lag in approaching that diversity and efficacy. Co-optimization of artificial creatures' morphology and control in silico shows promise for applications in physical soft robotics and virtual character creation;such approaches, however, require developing new learning algorithms that can reason about function atop pure structure. In this paper, we present Diffuse-Bot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. DiffuseBot bridges the gap between virtually generated content and physical utility by (i) augmenting the diffusion process with a physical dynamical simulation which provides a certificate of performance, and ii) introducing a co-design procedure that jointly optimizes physical design and control by leveraging information about physical sensitivities from differentiable simulation. We showcase a range of simulated and fabricated robots along with their capabilities.

关键词： Adversarial machine learning

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DDAP: Dual-Domain Anti-Personalization against Text-to-Image Diffusion Models 18

DDAP: Dual-Domain Anti-Personalization against Text-to-Image...

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18th IEEE International Joint conference on Biometrics, IJCB 2024

作者： Yang, Jing Xi, Runping Lai, Yingxin Lin, Xun Yu, Zitong Northwestern Polytechnical University China Great Bay University China Beihang University China

ISBN: (纸本)9798350364132

Diffusion-based personalized visual content generation technologies have achieved significant breakthroughs, allowing for the creation of specific objects by just learning from a few reference photos. However, when misused to fabricate fake news or unsettling content targeting individuals, these technologies could cause considerable societal harm. To address this problem, current methods generate adversarial samples by adversarially maximizing the training loss, thereby disrupting the output of any personalized generation model trained with these samples. However, the existing methods fail to achieve effective defense and maintain stealthiness, as they overlook the intrinsic properties of diffusion models. In this paper, we introduce a novel Dual-Domain Anti-Personalization framework (DDAP). Specifically, we have developed Spatial Perturbation learning (SPL) by exploiting the fixed and perturbation-sensitive nature of the image encoder in personalized generation. Subsequently, we have designed a Frequency Perturbation learning (FPL) method that utilizes the characteristics of diffusion models in the frequency domain. The SPL disrupts the overall texture of the generated images, while the FPL focuses on image details. By alternating between these two methods, we construct the DDAP framework, effectively harnessing the strengths of both domains. To further enhance the visual quality of the adversarial samples, we design a localization module to accurately capture attentive areas while ensuring the effectiveness of the attack and avoiding unnecessary disturbances in the background. Extensive experiments on facial benchmarks have shown that the proposed DDAP enhances the disruption of personalized generation models while also maintaining high quality in adversarial samples, making it more effective in protecting privacy in practical applications. © 2024 IEEE.

关键词： Adversarial machine learning

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artificial intelligence in Cybersecurity: applications, Challenges, and Future Developments

Artificial Intelligence in Cybersecurity: Applications, Chal...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Mouhamadou Mame Diop Mamadou Ba Khalifa Sylla Samuel Ouya Higher Polytechnic School - LITA University Cheikh Anta Diop of Dakar Dakar Senegal Cheikh Hamidou KANE Digital University (UN-CHK) Dakar Senegal

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Cybersecurity is facing increasingly sophisticated threats, such as ransomware, complex DDoS attacks, and zero-day attacks. These evolving threats reveal the limitations of traditional detection, prevention, and response methods. In this context, the integration of artificial intelligence (AI) emerges as a promising solution. machine learning (ML) and Deep learning (DL) algorithms enable enhanced detection of malicious behaviors, real-time anomaly identification, and the anticipation of cyberattacks before they cause significant damage. However, the adoption of AI in cybersecurity introduces new challenges, such as adversarial attacks targeting AI models, the lack of transparency in algorithmic decision-making, and the dependence on data quality. This document reviews the current applications of AI in cybersecurity, highlighting its contributions, limitations, and future perspectives. It also explores emerging solutions to counter zero-day attacks, the development of human-AI collaborative systems for adaptive defense, and the establishment of ethical and regulatory frameworks to ensure the responsible use of AI, aimed at protecting critical systems in the long term against increasingly sophisticated threats.

关键词： Deep learning Ethics machine learning algorithms Reviews Prevention and mitigation Denial-of-service attack Threat assessment Real-time systems Ransomware artificial intelligence

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One Step Large-Scale Multi-view Subspace Clustering Based on Orthogonal Matrix Factorization with Consensus Graph learning 6th

One Step Large-Scale Multi-view Subspace Clustering Based on...

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6th Chinese conference on Pattern Recognition and Computer Vision (PRCV)

作者： Zhang, Xinrui Li, Kai Peng, Jinjia Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Hebei Machine Vis Engn Res Ctr Baoding Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Multi-view clustering has always been a widely concerned issue due to its wide range of applications. Since real-world datasets are usually very large, the clustering problem for large-scale multi-view datasets has always been a research hotspot. Most of the existing methods to solve the problem of large-scale multi-view data usually include several independent steps, namely anchor point generation, graph construction, and clustering result generation, which generate the inflexibility anchor points, and the process of obtaining the cluster indicating matrix and graph constructing are separating from each other, which leads to suboptimal results. Therefore, to address these issues, a one-step multi-view subspace clustering model based on orthogonal matrix factorization with consensus graph learning(CGLMVC) is proposed. Specifically, our method puts anchor point learning, graph construction, and clustering result generation into a unified learning framework, these three processes are learned adaptively to boost each other which can obtain flexible anchor representation and improve the clustering quality. In addition, there is no need for post-processing steps. This method also proposes an alternate optimization algorithm for convergence results, which is proved to have linear time complexity. Experiments on several real world large-scale multi-view datasets demonstrate its efficiency and scalability.

关键词： Multi-view clustering Self-expressive subspace clustering Large-scale datasets Orthogonal matrix factorization

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A Smart Automated Roller Clamp for iv Therapy

A Smart Automated Roller Clamp for IV Therapy

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artificial intelligence and machine learning applications Theme: Healthcare and Internet of Things (AIMLA), International conference on

作者： N Arunkumar G K Jakir Hussain V. Bharath S Mahalakshmi S L Siva Dharshini M Rubina Begam Department of Chemical Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Mepco Schlenk Engineering College Sivakasi India Department of Biomedical Engineering Dhanalakshmi Srinivasan Engineering College Perambalur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Intravenous (iv) therapy is a critical aspect of medicinal care, involving the administration of solutions and medications in a straight line keen on patient’s bloodstream through a blood vessel. iv therapy is essential for treating a wide range of conditions, from dehydration to infections, and can be administered in various settings, including hospitals, clinics, and home care. One critical component of iv therapy is the roller clamp, which controls the flow of fluids and medications through the iv line. Traditional roller clamps require manual adjustment by healthcare professionals, which can lead to variability in the rate of flow and potential dosing errors. To address this challenge, the development of a smart automated roller clamp for iv therapy has emerged. Though, it is a small innovation it has enormous benefits to both healthcare providers and patients and can save one’s life. The smart automated roller clamp for iv therapy has the potential to prevent two critical complications associated with iv therapy Keywords: Classification, Backflow of blood, Air embolism

关键词： Technological innovation Fluids Hospitals Medical treatment Manuals machine learning Clamps

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An Integrated Adversarial Fine-tuning Method for Improving the Safety of Fault Diagnosis

An Integrated Adversarial Fine-tuning Method for Improving t...

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International conference on Industrial artificial intelligence (IAI)

作者： Juanru Zhao Ning Li Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Fault diagnosis is one of the key parts in the industrial field, and the current intelligent fault diagnosis meth-ods based on machine learning have achieved a wide range of applications. Although more research exists on this issue, less research has been done on the security of fault diagnosis methods. The security vulnerability of machine learning brings potential risks to fault diagnosis, which may lead to unanticipated property damage and casualties. Current defense methods based on adversarial training cause a degradation of standard accuracy while improving the robustness of machine learning models, which hinders the application of adversarial defense in the field of fault diagnosis. To address this issue, this study proposes an integrated adversarial fine-tuning method, which generates rep-resentative adversarial samples by integrating multiple methods, and uses the adversarial samples to fine-tune the vulnerable fault diagnosis model, thus realizing the improvement of the adversarial robustness of fault diagnosis while guaranteeing the standard accuracy. The effectiveness of the proposed method in this study is demonstrated through well-designed experiments based on publicly available datasets.

关键词： Fault diagnosis Training Degradation Accuracy Neural networks machine learning Robustness Safety Security Standards

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Exacerbating Algorithmic Bias through Fairness Attacks 35

Exacerbating Algorithmic Bias through Fairness Attacks

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35th AAAI conference on artificial intelligence / 33rd conference on Innovative applications of artificial intelligence / 11th Symposium on Educational Advances in artificial intelligence

作者： Mehrabi, Ninareh Naveed, Muhammad Morstatter, Fred Galstyan, Aram Univ Southern Calif Los Angeles CA 90007 USA Informat Sci Inst Marina Del Rey CA 90292 USA

ISBN: (纸本)9781577358664

Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been properly addressed. Indeed, most adversarial machine learning has focused on the impact of malicious attacks on the accuracy of the system, without any regard to the system's fairness. We propose new types of data poisoning attacks where an adversary intentionally targets the fairness of a system. Specifically, we propose two families of attacks that target fairness measures. In the anchoring attack, we skew the decision boundary by placing poisoned points near specific target points to bias the outcome. In the influence attack on fairness, we aim to maximize the covariance between the sensitive attributes and the decision outcome and affect the fairness of the model. We conduct extensive experiments that indicate the effectiveness of our proposed attacks.

关键词： learning algorithms

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