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Trajectory Optimization for Propulsion Energy Minimization of UAV Data Collection
As a flexible communication manner, unmanned aerial vehicle (UAV) communication is a promising technology for wireless communication systems....
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Data minimization for GDPR compliance in machine learning models
The EU General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA) mandate the principle of data minimization , which...
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Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs
The purpose of regulatory data minimization obligations is to limit personal data to the absolute minimum necessary for a given context. Beyond the...
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Discordance minimization-based imputation algorithms for missing values in rating data
Ratings are frequently used to evaluate and compare subjects in various applications, from education to healthcare, because ratings provide succinct...
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Towards a Formal Approach for Data Minimization in Programs (Short Paper)
As more and more processes are digitized, the protection of personal data becomes increasingly important for individuals, agencies, companies, and...
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Anonymization Between Minimization and Erasure: The Perspectives of French and Italian Data Protection Authorities
Two years after the General Data Protection Regulation (GDPR) went into effect, data anonymization remains one of the main issues linked to...
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A semi-automated BPMN-based framework for detecting conflicts between security, data-minimization, and fairness requirements
Requirements are inherently prone to conflicts. Security, data-minimization, and fairness requirements are no exception. Importantly, undetected...
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Latency-Aware Data Placements for Operational Cost Minimization of Distributed Data Centers
A large amount of data are stored in geographically distributed data centers interconnected by the Internet. The power consumption for running the...
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Sharpness-Aware Minimization for Out-of-Distribution Generalization
Machine learning models often suffer from a significant decline in performance when they encounter out-of-distribution (OOD) data that differs from...
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Hardware Implementation of Code Converters Designed to Reduce the Length of Binary Encoded Words
AbstractThe problems of synthesis of combinational circuits of code converters designed to reduce the length of words from a given set of encoded...
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Hyperparameter selection for Discrete Mumford–Shah
This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete...
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Property Constrained Video Summarization via Regret Minimization
Video summarization has become one of the most effective solutions for quickly understanding a large amount of video data. Video properties such as...
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Personalized Data Minimization Assurance Using Bluetooth Low Energy
Mobile identity applications allow people to use a mobile phone as a form of secure digital identity (ID) card for identification purposes. In this...
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Hook-in Privacy Techniques for gRPC-Based Microservice Communication
gRPC is at the heart of modern distributed system architectures. Based on HTTP/2 and Protocol Buffers, it provides highly performant, standardized,...
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Low rank and sparse decomposition based on extended \({LL}_{p}\) norm
The problem of decomposing a given matrix into its low-rank and sparse components, known as robust principle component analysis (RPCA), has found...
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Enhancing Continual Noisy Label Learning with Uncertainty-Based Sample Selection and Feature Enhancement
The task of continual learning is to design algorithms that can address the problem of catastrophic forgetting. However, in the real world, there are...
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Algorithmic generalization ability of PALM for double sparse regularized regression
We propose a novel double sparse regularized modelling paradigm for Generalized Linear Model in high-dimensional setting, where we allow the...
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Lifelong learning with selective attention over seen classes and memorized instances
Catastrophic forgetting challenges lifelong classification learning of modern neural networks, especially when observations arrive from a data stream...
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An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method
Extreme learning machine (ELM) is one of the most notable machine learning algorithms with many advantages, especially its training speed. However,...
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Deep Mutual Distillation for Semi-supervised Medical Image Segmentation
In this paper, we focus on semi-supervised medical image segmentation. Consistency regularization methods such as initialization perturbation on two...