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AI for precision medicine must keep non-random complexity in mind to support equity in outcomes

AI for precision medicine must keep non-random complexity in...

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ieee international conference on e-Science and Grid computing

作者： Benjamin L. Smarr Shu Chien - Gene Lay Department of Bioegnineering & the Halicioğlu Data Science Institute University of California San Diego La Jolla CA USA

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Large Models (LMs) as a new form of artificial intelligence (AI) have the potential to provide more personal insights by processing large volumes of multimodal, longitudinal data now generated by hundreds of millions of people through things like wearables, apps, and centralized systems such as electronic medical records. Medical research has historically excluded most populations (women and minorities) to the effect that treatments are routinely less effective and sometimes harmful in these populations. Machine Learning (ML) in medicine was supposed to solve these problems, but in many cases exacerbated these harmful historical biases. LMs project an illusion of understanding that might lead some to repeat the omissions of the past, and once again exacerbate unfair and harmful outcomes. But human physiology is both deeply complex and structured. Health AI researchers can incorporate insights from both biology and historical failures to help Health AIs realize the goal of equitable health support more completely than previous efforts.

关键词： Electric potential Precision medicine Machine learning Physiology data models complexity theory Wearable devices

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Framework for Effective Information Security Investments: A data-Driven Model Recommendation for Information Security Investment Decision-Making

Framework for Effective Information Security Investments: A ...

引用

2025 ieee international conference on Big data and Smart computing, BigComp 2025

作者： Park, Byeongjo Kim, Tae-Sung Chungbuk National University Dept. of Convergence Security Chungbuk Korea Republic of Chungbuk National University Dept. of Management Information System Chungbuk Korea Republic of

The rapid advancement of information and communication technology has increased work efficiency but also introduced complex security challenges for all stakeholders. While companies have become more aware of the importance of information security, they face difficulties in making effective security investment decisions due to budget constraints and a shortage of skilled security personnel. This study proposes a data-driven framework to support information security investment decision-making. The proposed framework employs quantitative models such as Genetic Algorithm, and Game Theory to select the optimal model based on specific input parameters. The framework systematically categorizes key input parameters necessary for risk assessment and countermeasure selection, guiding users through the optimization process by choosing the most suitable decision-making model. This structured approach enables organizations to select cost-effective security measures within a given budget. Additionally, the framework provides actionable insights by presenting results in visualizations and detailed reports, enhancing investment efficiency. Furthermore, it can be used as a tool by security managers to justify budget requests and assists executives in making data-driven information security decisions. This framework ultimately aims to streamline the information security investment process, enabling organizations to mitigate risks effectively within limited resources. © 2025 ieee.

关键词： Risk assessment

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Zero-shot LLM-guided Counterfactual Generation: A Case Study on NLP Model Evaluation

Zero-shot LLM-guided Counterfactual Generation: A Case Study...

引用

ieee international conference on Big data

作者： Amrita Bhattacharjee Raha Moraffah Joshua Garland Huan Liu School of Computing and AI Arizona State University Tempe AZ USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Global Security Initiative Arizona State University Tempe AZ USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

With the development and proliferation of large, complex, black-box models for solving many natural language processing (NLP) tasks, there is also an increasing necessity of methods to stress-test these models and provide some degree of interpretability or explainability. While counterfactual examples are useful in this regard, automated generation of counterfactuals is a data and resource intensive process, that may be infeasible in practice, especially for new tasks and data domains. Therefore, in this work we explore the possibility of leveraging large language models (LLMs) for zero-shot counterfactual generation in order to stress-test NLP models. We propose a structured pipeline to facilitate this generation, and we hypothesize that the instruction-following and textual understanding capabilities of recent LLMs can be effectively leveraged for generating high quality counterfactuals in a zero-shot manner, without requiring any training or fine-tuning. Through comprehensive experiments on a variety of propreitary and open-source LLMs, along with various downstream tasks in NLP, we explore the efficacy of LLMs as zero-shot counterfactual generators in evaluating and explaining black-box NLP models. 1

关键词： Training Large language models Pipelines Closed box Big data Natural language processing data models Generators

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Visual Explanations from Deep Networks Using GradCAM in Federated Learning

Visual Explanations from Deep Networks Using GradCAM in Fede...

引用

ieee international conference on Research, Innovation and Vision for the Future

作者： Anh-Tu Tran Trong-Nghia Mai Van-Quyet Nguyen Academy of Cryptography Techniques

ISBN: (数字)9798331505073

ISBN: (纸本)9798331505080

In the realm of Explainable Artificial Intelligence (XAI), visual explanations play a crucial role in demystifying complex models, providing insights into their decision-making processes. This paper explores the integration of Federated Learning (FL) as a method to enhance privacy without compromising the quality of visual explanations. FL, by design, allows multiple decentralized devices to collaboratively train models while keeping the data localized, thereby significantly mitigating privacy concerns. We specifically evaluate the impact of FL on visual explanations generated by Gradient-weighted Class Activation Mapping (Grad-CAM), a popular technique for producing class-discriminative localization maps. Our study demonstrates that while FL introduces certain challenges, such as heterogeneity of data across different devices, it does not substantially degrade the interpretability of models as visualized by Grad-CAM. This research highlights the feasibility and benefits of combining FL with visual explanation techniques, paving the way for more secure and interpretable AI systems.

关键词： Location awareness Visualization Privacy data privacy Federated learning Explainable AI Computational modeling Decision making data visualization data models

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Multimodal Steganography: A Comparative Analysis of LSB and DCT Methods for Image and Audio data Concealment

Multimodal Steganography: A Comparative Analysis of LSB and ...

引用

international conference for Convergence of Technology (I2CT)

作者： Bathula Achutha Dharani Bhoomireddy Yashaswini Garimi Reddy Shyashyankha Reddy Shinu M. Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Multimodal steganography is a technique that involves concealing information in multiple types of media to enhance data-hiding capabilities. In this proposed work, exploration and comparison of two distinct methods for multimodal steganography are performed: the LSB method and the DCT method for the modalities. The LSB method is straightforward to implement and has a high data capacity, but it is susceptible to detection and quality degradation. In contrast, the DCT method, while more complex, offers better robustness and perceptual quality, particularly for larger data payloads. The comparison considers factors such as capacity, robustness, perceptual quality, and implementation complexity. The findings suggest that the choice between the two methods depends on specific application requirements. LSB may be suitable for scenarios prioritizing higher data capacity, while DCT is recommended for applications requiring enhanced robustness and perceptual quality. The abstract concludes by proposing the potential use of a hybrid approach for optimal trade-offs between capacity and security.

关键词： Degradation Resistance Steganography Media Robustness Discrete cosine transforms Security

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Parallel Systems Framework: A Safe and Efficient Testbed for Unmanned Ships

Parallel Systems Framework: A Safe and Efficient Testbed for...

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Digital Twins and Parallel Intelligence (DTPI), ieee international conference on

作者： Long Fu Zhen Shen Xisong Dong Yunjun Han Shuguang Yang Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Schoool of Rail Transportation Shandong Jiaotong University Jinan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements.

关键词： Accuracy Oceans Virtual environments Digital twins Marine vehicles Testing

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Measuring Spoken English Proficiency Level Based on IELTS Speaking Test Using Machine Learning Models

Measuring Spoken English Proficiency Level Based on IELTS Sp...

引用

ieee WIC ACM international conference on Web Intelligence (WI)

作者： Dylan Mac Yves Jonathan Hoyin Chan Innovative Cognitive Computing Research Center School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

This study presents the development of a system for evaluating English-speaking proficiency based on the IELTS framework. We employed data collection, analysis, and machine learning techniques to build a predictive model. The system focuses on using the statistical features extracted from the transcription of the speaking test. The initial model (XGBoost) showed a mean squared error of 2.29. After feature reduction and hyperparameter tuning, performance improved, achieving an average cross-validation score of 1.57 with a variance of 0.3872 across five folds. Our analysis found that lexical diversity, measured by the total number of unique words, is the most influential factor in predicting IELTS scores, while other features had minimal impact. Despite the improvements, the model does not account for critical speech components like fluency and pronunciation. Future research should address these aspects to better align with IELTS criteria. This study contributes to natural language processing and linguistics by offering a new machine learning model for predicting IELTS scores from the test transcription.

关键词： Machine learning Predictive models Linguistics data collection Feature extraction Speech Natural language processing Time factors Speech processing Tuning

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MFTCN: A Multi-Frequency Temporal Convolutional Network for Enhancing Temperature Prediction Dependability

MFTCN: A Multi-Frequency Temporal Convolutional Network for ...

引用

ieee international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Wei Huang Yuan Gao Xi angling Fu Zhiyi Song Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Huazhong University of Science and Technology Hubei China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Accurate temperature prediction plays a key role in various fields such as agriculture, ecology, and the environment. Traditional methods directly learn historical temperature changes to predict future temperature values. However, temperature changes are complex, and traditional methods are difficult to cope with nonlinear changes in temperature, resulting in poor temperature prediction accuracy. To address this issue, we attempt to decompose complex temperature changes into several simple components and propose a multi-frequency temporal convolutional network for short-term temperature prediction. Specifically, three key modules, namely multi - frequency decomposition, component prediction, and parameter optimization are employed to reduce the complexity of temperature changes and the uncertainty of prediction while ensuring that the original temperature information is not lost. On two real temperature datasets, our proposed method outperforms five baselines, which demonstrates that decomposing complex temperature changes is beneficial to improving temperature prediction accuracy.

关键词： Temperature distribution Temperature dependence Accuracy Uncertainty Biological system modeling Predictive models data models complexity theory Convolutional neural networks Optimization

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Novel Adaptive and Iterative Multi-density DBSCAN Method for Space Signal Sorting in complex Electromagnetic Environment

Novel Adaptive and Iterative Multi-density DBSCAN Method for...

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ieee international conference on Communication, Networks and Satellite (ComNetSat)

作者： Yi Wei Dongping Cao Luyuan Cui Chao Li Shang-Rong Ou-Yang Shanghai Aerospace Electronic Technology Institute Shanghai China Shanghai Key Laboratory of Collaborative Computing in Spacial Heterogeneous Networks (CCSN) Shanghai China

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

Due to the exponential growth of wireless devices, recent years have witnessed a dramatic increase in the density and complexity of the space electromagnetic environment. As a crucial task in the field of space cognitive spectrum sensing, the emitter signal sorting (ESS) task becomes increasingly challenging in such complex electromagnetic environments. Especially when the pulse description word (PDW) dataset of the multiple received emitters exhibits extremely different density level, the existing clustering based ESS methods fail to acquire the satisfactory performance due to its single pair of adjustable parameters. To overcome this difficulty, this work formulates the ESS task into a multi-density clustering problem for unevenly-distributed dataset. By exploiting the idea of density-based spatial clustering of applications with noise (DBSCAN), we propose a novel adaptive and iterative multidensity (AIMD)-DBSCAN based ESS method, which is able to automatically determine a series of key parameter pairs corresponding to different density level and perform a specially-designed iterative DBSCAN algorithm to find new clusters or merge to existing ones in descending density order. Simulation results have demonstrated that the proposed method can achieve better performance than its counterparts in terms of clustering accuracy and normalized mutual information (NMI) in different scenarios.

关键词： Wireless communication Wireless sensor networks Satellites Simulation Noise Iterative algorithms Sensors Electromagnetics Mutual information Sorting

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Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network

Research on Deep Learning Model of Feature Extraction Based ...

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Image Processing and Computer Applications (ICIPCA), ieee international conference on

作者： Houze Liu Iris Li Yaxin Liang Dan Sun Yining Yang Haowei Yang Department Computer Science and Engineering New York University New York USA Graduate School of Art & Science New York University New York USA Viterbi School of Engineering University of Southern California Los Angeles USA Department of Electrical and Systems Engineering Washington University St. Louis USA School of Computer Science Carnegie Mellon University Pittsburgh USA Cullen College of Engineering University of Houston Houston USA

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode.

关键词： Deep learning Pneumonia Accuracy Sensitivity Computational modeling Feature extraction Convolutional neural networks data mining Medical diagnostic imaging Standards

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