Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning

Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, privacy protection. The recently proposed Federated Learning (FL) frame-work allows learning a shared model collaboratively without data being centralized or data sharing among data owners. However, we show in this paper that the generalization ability of the joint model is poor on Non-Independent and Non-Identically Dis-tributed (Non-IID) data, particularly when the Federated Averaging (FedAvg) strategy is used in this collaborative learning framework thanks to the weight divergence phenomenon. We propose a novel boosting algorithm for FL to address this generalisation issue, as well as achieving much faster convergence in gradient based optimization. We demonstrate our Federated Boosting (FedBoost) method on privacy-preserved text recognition, which shows significant improvements in both performance and efficiency. The text images are based on publicly available datasets for fair comparison and we intend to make our implementation public to ensure reproducibility.Comment: The paper has been submitted to BMVC2020 on April 30t

AHP Aided Decision-Making in Virtual Machine Migration for Green Cloud

In this study, an analytical hierarchy process based model is proposed to perform the decision-making for virtual machine migration towards green cloud computing. The virtual machine migration evaluation index system is established based on the process of constructing hierarchies for evaluation of virtual machine migration, and selection of task usage. A comparative judgment of two hierarchies has been conducted. In the experimental study, five-point rating scale has been adopted to map the raw data to the scaled rating score; this rating method is used to analyze the performance of each virtual machine and its task usage data. The results show a significant improvement in the decision-making process for the virtual machine migration. The deduced results are useful for the system administrators to migrate the exact virtual machine, and then switch on the power of physical machine that the migrated virtual machine resides on. Thus the proposed method contributes to the green cloud computing environment

Efficient Geometric Correction Workflow for Airborne Hyperspectral Images through DEM-Driven Correction Techniques

Geometric correction, a pivotal step in the preprocessing of airborne remote sensing imagery, is critical for ensuring the accuracy of subsequent quantitative analyses. Achieving precise and efficient geometric correction for airborne hyperspectral data remains a significant challenge in the field. This study presents a new method for system-level and fine-scale geometric correction of uncontrolled airborne images utilizing DEM data, which integrates forward and inverse transformation algorithms. Furthermore, an optimized workflow is proposed to facilitate the processing of large-scale hyperspectral datasets. The effectiveness of the proposed method is demonstrated through an application analysis using airborne HyMap imagery, with experimental outcomes indicating high application accuracy and enhanced processing efficiency

Justice at the forefront: cultivating felt accountability towards Artificial Intelligence among healthcare professionals

The advent of AI has ushered in a new era of patient care, but with it emerges a contentious debate surrounding accountability for algorithmic medical decisions. Within this discourse, a spectrum of views prevails, ranging from placing accountability on AI solution providers to laying it squarely on the shoulders of healthcare professionals. In response to this debate, this study, grounded in the mutualistic partner choice (MPC) model of the evolution of morality, seeks to establish a configurational framework for cultivating felt accountability towards AI among healthcare professionals. This framework underscores two pivotal conditions: AI ethics enactment and trusting belief in AI and considers the influence of organizational complexity in the implementation of this framework. Drawing on Fuzzy-set Qualitative Comparative Analysis (fsQCA) of a sample of 401 healthcare professionals, this study reveals that a) focusing justice and autonomy in AI ethics enactment along with building trusting belief in AI reliability and functionality reinforces healthcare professionals’ sense of felt accountability towards AI, b) fostering felt accountability towards AI necessitates ensuring the establishment of trust in its functionality for high complexity hospitals, and c) prioritizing justice in AI ethics enactment and trust in AI reliability is essential for low complexity hospitals

Domain Altering SNPs in the Human Proteome and Their Impact on Signaling Pathways

Single nucleotide polymorphisms (SNPs) constitute an important mode of genetic variations observed in the human genome. A small fraction of SNPs, about four thousand out of the ten million, has been associated with genetic disorders and complex diseases. The present study focuses on SNPs that fall on protein domains, 3D structures that facilitate connectivity of proteins in cell signaling and metabolic pathways. We scanned the human proteome using the PROSITE web tool and identified proteins with SNP containing domains. We showed that SNPs that fall on protein domains are highly statistically enriched among SNPs linked to hereditary disorders and complex diseases. Proteins whose domains are dramatically altered by the presence of an SNP are even more likely to be present among proteins linked to hereditary disorders. Proteins with domain-altering SNPs comprise highly connected nodes in cellular pathways such as the focal adhesion, the axon guidance pathway and the autoimmune disease pathways. Statistical enrichment of domain/motif signatures in interacting protein pairs indicates extensive loss of connectivity of cell signaling pathways due to domain-altering SNPs, potentially leading to hereditary disorders

Commutative L*-rings II

It is shown that for several important classes of commutative rings, L* and O* are equivalent. In particular, a commutative artinian ring is L* if and only if it is O*. More examples of O*-fields are provided.Mathematics Subject Classification (2010): Primary 06F25.Keywords: Lattice order, partial order, regular division closed, total order, F*-ring,L*-ring, O*-rin

Research on Vibration Behavior of the Plate-Like Joint Interfaces Based on Comprehensive Unit Stiffness Matrix

Abstract: The dynamic characteristics of joint interfaces have significant effect on both static and dynamic behaviors of the whole machine tool structures. Its dynamic model can be simplified as a group of equivalent spring-damping elements, that is, an ‘elastic interlayer ’ without mass. The unit area dynamic characteristic parameters are the key to analyze the dynamic properties of joint interfaces. However, when building the stiffness and damping matrices of the joint interfaces, the interaction between the ‘elastic interlayer ’ and the conjunctions are always ignored, which leads to errors arising. A test system for identifying the unit area dynamic characteristic parameters of different kinds of joint interfaces are represented based on Equivalent Single Degree Of Freedom (ESDOF) system theory. The unit area dynamic characteristic parameters can be applied under all kinds of conditions (including different materials, pre-tightening force, surface roughness, lubricating conditions, media, etc). Then a comprehensive unit stiffness matrix is derived from these parameters. The presented method is compared with the conventional method on analyzing the vibration behavior of an assembled beam structure. The comparison results show that the presented method is in excellent agreement on the actual conditions, which has an obvious advantage on accuracy. The presented method can be used as an effective way for precisely analyzing vibration behavior of complicated mechanical structures with plate-like joint interfaces. Copyright © 2014 IFSA Publishing, S. L

Evaluation and Intercomparison of Topographic Correction Methods Based on Landsat Images and Simulated Data

Topographic effects in medium and high spatial resolution remote sensing images greatly limit the application of quantitative parameter retrieval and analysis in mountainous areas. Many topographic correction methods have been proposed to reduce such effects. Comparative analyses on topographic correction algorithms have been carried out, some of which drew different or even contradictory conclusions. Performances of these algorithms over different terrain and surface cover conditions remain largely unknown. In this paper, we intercompared ten widely used topographic correction algorithms by adopting multi-criteria evaluation methods using Landsat images under various terrain and surface cover conditions as well as images simulated by a 3D radiative transfer model. Based on comprehensive analysis, we found that the Teillet regression-based models had the overall best performance in terms of topographic effects’ reduction and overcorrection; however, correction bias may be introduced by Teillet regression models when surface reflectance in the uncorrected images do not follow a normal distribution. We recommend including more simulated images for a more in-depth evaluation. We also recommend that the pros and cons of topographic correction methods reported in this paper should be carefully considered for surface parameters retrieval and applications in mountain regions