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Exploring data Poisoning Attacks Against Adversarially Trained Skin Cancer Diagnostics

Exploring Data Poisoning Attacks Against Adversarially Train...

引用

ieee international conference on Utility and Cloud computing (UCC)

作者： Anum Paracha Junaid Arshad Mohamed Ben Farah Khalid Ismail Birmingham City University UK

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Machine Learning(ML) models have proven to be effective in automating digital systems such as in healthcare for early and accurate diagnosis for early treatment. However, ML models are also threatened by several adversarial attacks including poisoning, evasion and inference attacks. As with any other digital system, the defense approaches for ML-based systems are engaged in a continuous struggle with adversaries - new attack vectors are identified and developed frequently requiring continuous efforts to develop methods to mitigate against them. This study investigates the effectiveness of adversarial training against data poisoning attacks in traditional ML models. We have analyzed the sensitivity of baseline machine learning models in non-complex settings. Our research highlighted that although adversarial training is very effective in securing a neural network against a new attack, it failed to protect other traditional machine learning models i.e. SVM and Random Forest. With 10% data poisoning attack, the adversarially trained SVM and random forest models’ accuracy decreased to 51.2% and 27.5%, respectively. Whereas, the accuracy of neural networks only degraded by 2%. We have also analyzed the model variance with data poisoning and their diagnosis behavior with random sampling. Through random sampling, it is further highlighted that SVM is the most affected model with the 100% change in the individual sample diagnosis however random forest and neural networks observed minor changes in their confidence scores, per sample predictions.

关键词： Support vector machines Training Analytical models Accuracy Digital systems Computational modeling Neural networks data models Random forests Skin cancer

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CuttleFlow: Infrastructure-Specific Workflow Adaption for Improved Reusability

CuttleFlow: Infrastructure-Specific Workflow Adaption for Im...

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ieee international conference on e-Science and Grid computing

作者： Ninon De Mecquenem Simon Bosse Vasilis Bountris Somayeh Mohammadi Knut Reinert Ulf Leser Humboldt-Universität zu Berlin Berlin Germany Humboldt-Universität zu Berlin Germany Freie Universität Berlin Germany

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Scientific workflows have gained popularity for large-scale data analysis due to their potential to improve the reproducibility, scalability and documentation of complex multistep scientific analysis pipelines. However, their reusability is currently limited in practice, as a workflow is typically developed for a specific infrastructure. This is reflected in the choice of tools (e.g. less/more memory requirements), their configuration (e.g. number of threads) and the workflow topology (e.g. data parallel scatter/gather). Re-running such a workflow requires access to the same, or at least a highly similar, computing environment, effectively reducing its use by other groups. To address this challenge, we present CuttleFlow, a novel method for adapting and rewriting scientific workflows given a description of an infrastructure and its inputs. CuttleFlow starts from an abstract workflow description and compiles it into an infrastructure-specific logical workflow using three types of rewriting operations, namely tool replacement, tool reconfiguration, and data scattering/gathering for task parallelization. We implement a prototype based on NextFlow and evaluate it for two important bioinformatics data analysis problems, namely RNAseq and metagenomics, on a distributed infrastructure. We demonstrate the large impact that the rewriting of CuttleFlow can have on runtime, achieving a reduction in makespan of up to 71%. We also demonstrate a significant reduction in resource usage through our rewriting approach.

关键词： Metagenomics data analysis Runtime Scalability Instruction sets Pipelines Memory management

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Research and application of data encryption transmission method for power cloud-edge collaborative control system

Research and application of data encryption transmission met...

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ieee international conference on Energy Internet (ICEI)

作者： Jie Zhou Ruifeng Ding Zhuang Shao Lijun Lei Rundian Energy Science and Technology Co. Ltd China Resources Power Holdings Co. Ltd. Zhengzhou China

ISBN: (数字)9798331523558

ISBN: (纸本)9798331523565

With the large-scale access of distributed new energy power generation systems such as photovoltaic and wind power, the power system is experiencing a significant increase in data volume and control complexity. Its data processing and control are increasingly dependent on the powerful computing power of the cloud and the real-time response capability of the edge. However, ensuring secure and real-time transmission of data remains a dual challenge for cloud-edge collaboration. To address these challenges, this paper proposes a data encryption transmission method for real-time control systems in distributed power generation based on a cloud-edge collaborative architecture. Firstly, we construct a mathematical model to evaluate the real-time performance of such systems. Then, leveraging fuzzy control theory along with Bcrypt and BshAES encryption algorithms, we design a dynamic adaptive adjustment mechanism that maximizes system security while maintaining real-time response capability. This method overcomes limitations associated with traditional methods that involve high costs or complex management due to hardware upgrades or increased bandwidth requirements aimed at improving security and real-time performance. The practical application shows that the proposed scheme can effectively enhance the overall security of the system on the basis of ensuring the real-time performance of the system, and provide new ideas and technical support for the optimization of distributed energy systems in the future.

关键词： Cloud computing Fuzzy control Heuristic algorithms Power system dynamics Collaboration Distributed databases Real-time systems Encryption Security Optimization

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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ieee international conference on Joint Cloud computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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Interactive Visualization of BioMedical data

Interactive Visualization of BioMedical Data

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ieee international conference on Big data

作者： Mohammad Shafiqul Islam Rahat Ibn Rafiq Guenter Tusch College of Computing Grand Valley State University Michigan USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Public databases such as the NCBI Gene Expression Omnibus (GEO) house millions of experimental gene expression datasets invaluable for transcriptome meta-analysis, enabling researchers to identify genes, pathways, and biomarkers with enhanced accuracy. However, while these datasets offer great potential, finding relevant biological pathways in disease-related contexts remains challenging. To address this, we introduce a web-based visualization tool that utilizes PubMed abstracts to extract key biomedical entities, such as genes, pathways, diseases, species, and chemicals, and represent their relationships through an interactive graph. By leveraging PubMed’s annotated MeSH terms, our tool allows users to explore the literature more intuitively, bypassing traditional dataset search limitations and focusing on published results. The tool transforms complex query outputs into a graph-based network, providing a visual interface for navigating connections between biological entities. This interactive approach helps researchers to analyze relationships within large-scale biomedical data and uncover potential pathways for drug repurposing and disease understanding. We discuss the tool’s design using ***, its application in biomedical research, and the challenges faced when visualizing large datasets, offering new insights for improved data interpretation and exploration.

关键词： Drugs data visualization Biomarkers Biology Gene expression data mining Bioinformatics Chemicals Diseases Biomedical imaging

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Leveraging Soil Nutrient Patterns for Improved Crop Yield Through Random Forest Modeling

Leveraging Soil Nutrient Patterns for Improved Crop Yield Th...

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ieee international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olufunke Rebecca Vincent Peter Oluwaseun David Daniel Dauda Wisdom Gboyega Festus Akinboro Paul Adebambo Cecelia Ajowoh Adenusi Department of Computer Sciences Federal University of Agriculture Abeokuta (FUNAAB) Nigeria Department of Computer Sciences Chrisland University Abeokuta Ogun State Nigeria Department of Computer Sciences Federal University of Agriculture Abeokuta Ogun State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

This research explores the use of Random Forest modeling to improve crop yield predictions by analyzing soil nutrient patterns. Recognizing the critical role that nutrients such as nitrogen, phosphorus, and potassium play in plant growth, the study focuses on identifying complex interactions between soil health and crop productivity that traditional methods often overlook. By collecting and analyzing comprehensive soil data, a Random Forest model is developed to predict crop yields with greater accuracy. The results demonstrate how this machine learning (ML) approach can guide precision agriculture, helping farmers optimize fertilization and soil management to enhance crop yields while promoting more sustainable agricultural practices.

关键词： Analytical models Accuracy Training data Soil Predictive models Nutrients data models Reliability Crop yield Random forests

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Optimizing Split Algorithm Performance: A Heuristic Method for Enhanced Tensor Product Matrix Computations

Optimizing Split Algorithm Performance: A Heuristic Method f...

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ieee India conference (INDICON)

作者： Biswajit Bhowmik Sunil Kumar Sanjay Ramapogu Raju Anupam Prakash Omkar Mense Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Surathkal Mangalore Karnataka India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Optimizing tensor product matrix computations is critical for enhancing computational efficiency in high-performance applications. Traditional algorithms, like the Split algorithm, often struggle due to the unique properties of each matrix involved. This paper presents a novel heuristic method that optimizes the selection of cutting points and matrix ar-rangement, significantly reducing redundant calculations and minimizing memory usage. The proposed approach adapts to the varying characteristics of tensor products, improving performance across different computational scenarios. Enhancing floating-point operation efficiency and CPU utilization delivers substantial speed and efficiency gains, particularly in large-scale tensor product matrix operations, offering a robust solution for complex computational tasks.

关键词： Wireless sensor networks Tensors High dimensional data Scalability Heuristic algorithms High performance computing Robustness Computational efficiency Distributed computing Optimization

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AI-based Catalytic Performance Prediction for CO₂ Electrochemical Reduction using Ionic Liquids 50

AI-based Catalytic Performance Prediction for CO<sub>2</sub>...

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50th Annual conference of the ieee Industrial Electronics Society, IECON 2024

作者： Jaffari, Rabeea Saadaldeen Mohammed, Sulafa Abdalmageed Gunasekara, Lakshitha Mehran University of Engineering and Technology Software Engineering Department Sindh Jamshoro 76260 Pakistan Aalborg University Department of Energy Technology Aalborg 9220 Denmark La Trobe University Centre for Data Analytics and Cognition Australia

Recently, ionic liquids (ILs) have garnered remarkable attention as electrolytes for CO2 electrochemical reduction (CO2ER) due to their unique properties viz. thermal and chemical stability, good CO2 solubility, and t... 详细信息

Recently, ionic liquids (ILs) have garnered remarkable attention as electrolytes for CO₂ electrochemical reduction (CO₂ER) due to their unique properties viz. thermal and chemical stability, good CO₂ solubility, and their ability to reduce the reaction overpotential. The catalytic performance of ILs in CO₂ER has been explored via experimental methods which have limitations, given the unclear understanding of the complex reaction mechanisms. Recently, Artificial Intelligence (AI) methods have gained increased attention across diverse applications including chemical engineering. These methods play a pivotal role in extracting insights, understanding patterns, and mitigating uncertainty within datasets. Hence, in this study, we investigate two categories of AI models (ML and DL) for predicting the CO₂ER catalytic performance using ILs via Gibbs free energy and capacity. For this task, we formulate a novel dataset (CO₂ERIL) from 90 ILs in two formats using: (1) 30 electronic and geometric IL properties, (2) IL chemical structures in SMILES data format. The dataset versions are formulated using the Conductor-like Screening Model for Realistic Solvents (COSMO-RS) and TURBOMOLE software. The prediction results of the AI models on the two dataset versions depict a similar trend in the predicted target variables signifying that the dataset format does not really affect the model performance. Moreover, the best ML model outperforms the DL model for one target variable, Free energy predictions, but lags in the other target variable, capacity predictions. Our study is novel in terms of AI methodology as well as the CO₂ER dataset. Our research outcomes will contribute to the more effective selection of ILs for CO₂ER catalysis. © 2024 ieee.

关键词： AI CO2 electrochemical reduction ILs

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Modification Method for Internal Network Flow Anomaly Detection Based on Multi-timescale Analysis

Modification Method for Internal Network Flow Anomaly Detect...

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ieee international conference on data Science in Cyberspace (DSC)

作者： Xinyu Zhang Junmei Ding Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the continuous development of information technology, security issues from internal networks are becoming more and more important. Many anomaly detection algorithms are designed to identify anomalies, but these algorithms usually do not care about temporal information, or only care about fine-grained temporal information. On the other hand, deep-learning-based algorithms must rely on complex neural networks to capture temporal information, which is difficult to deploy and maintain in actual internal network environments. Addressing the aforementioned problems, this paper proposes a multi-timescale analyzer using wavelet packet transform and semi-supervised learning, and a method that uses the analyzer to modify the original anomaly scores called Multi-timescale Modification (MTM) method. The method is validated using the network flow dataset CIC-IDS 2017 and a real-world dataset. Experimental evaluations show that using the proposed method can obtain better performance than the original algorithms.

关键词： Hands Neural networks Cyberspace Semisupervised learning data science Wavelet analysis Wavelet packets Security Information technology Anomaly detection

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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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