MPHM: Model poisoning attacks on federal learning using historical information momentum

Federated learning(FL) development has grown increasingly strong with the increased emphasis on data for individuals and industry. Federated learning allows individual participants to jointly train a global model without sharing local data, which significantly enhances data privacy. However, federated learning is vulnerable to poisoning attacks by malicious participants. Since federated learning does not have access to the participants’ training process, i.e., attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants. Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible. In contrast, we propose a novel model poisoning attack based on the momentum of history information (MPHM), that is, the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training, which will make the new malicious updates more effective and stealthy. Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information. Experiments show that in the classical defense case, our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks

A Lightweight Sentiment Analysis Framework for a Micro-Intelligent Terminal

Sentiment analysis aims to mine polarity features in the text, which can empower intelligent terminals to recognize opinions and further enhance interaction capabilities with customers. Considerable progress has been made using recurrent neural networks or pre-trained models to learn semantic representations. However, recently published models with complex structures require increasing computational resources to reach state-of-the-art (SOTA) performance. It is still a significant challenge to deploy these models to run on micro-intelligent terminals with limited computing power and memory. This paper proposes a lightweight and efficient framework based on hybrid multi-grained embedding on sentiment analysis (MC-GGRU). The gated recurrent unit model is designed to incorporate a global attention structure that allows contextual representations to be learned from unstructured text using word tokens. In addition, a multi-grained feature layer can further enrich sentence representation features with implicit semantics from characters. Through hybrid multi-grained representation, MC-GGRU achieves high inference performance with a shallow structure. The experimental results of five public datasets show that our method achieves SOTA for sentiment classification with a trade-off between accuracy and speed

Evaluation of modeled global vegetation carbon dynamics: Analysis based on global carbon flux and above-ground biomass data

Dynamic global vegetation models are useful tools for the simulation of global carbon cycle. However, most models are hampered by the poor availability of global aboveground biomass (AGB) data, which is necessary for the model calibration process. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential AGB, at the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Model results showed that IBIS could reproduce GPP with acceptable accuracy in monthly and annual scales. At the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed AGB for pan-tropical forests. The bias in modeled AGB was mainly caused by the unchanged parameters over the global scale for a specific plant functional type. This study also showed that different meteorological inputs can introduce substantial differences in modeled AGB in the global scale, although this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB. (C) 2017 Elsevier B.V. All rights reserved

Assessing the Effects of Different Fillers and Moisture on Asphalt Mixtures’ Mechanical Properties and Performance

This laboratory study was conducted to comparatively assess the effects of different fillers and moisture on the mechanical properties and performance of asphalt mixtures. In the study, a typical Pen70 base asphalt was modified with four different filler materials, namely limestone powder, cement, slaked (hydrated) lime, and brake pad powder, to produce different asphalt mortars that were subsequently used to prepare the asphalt mixtures. Thereafter, various laboratory tests, namely dynamic uniaxial repeated compressive loading, freeze-thaw splitting, and semicircular bending (SCB) were conducted to evaluate the moisture sensitivity, high-temperature stability, low-temperature cracking, and fatigue performance of the asphalt mixtures before and after being subjected to water saturation conditions. Overall, the study results indicated superior moisture tolerance, water damage resistance, and performance for slaked (hydrated) lime, consecutively followed by brake pad powder, cement, and limestone powder. That is, for the materials evaluated and the laboratory test conditions considered, limestone mineral powder was found to be the most moisture-sensitive filler material, whilst slaked (hydrated) lime was the most moisture-tolerant and water-damage resistant filler material

Assessing the Effects of Different Fillers and Moisture on Asphalt Mixtures’ Mechanical Properties and Performance

This laboratory study was conducted to comparatively assess the effects of different fillers and moisture on the mechanical properties and performance of asphalt mixtures. In the study, a typical Pen70 base asphalt was modified with four different filler materials, namely limestone powder, cement, slaked (hydrated) lime, and brake pad powder, to produce different asphalt mortars that were subsequently used to prepare the asphalt mixtures. Thereafter, various laboratory tests, namely dynamic uniaxial repeated compressive loading, freeze-thaw splitting, and semicircular bending (SCB) were conducted to evaluate the moisture sensitivity, high-temperature stability, low-temperature cracking, and fatigue performance of the asphalt mixtures before and after being subjected to water saturation conditions. Overall, the study results indicated superior moisture tolerance, water damage resistance, and performance for slaked (hydrated) lime, consecutively followed by brake pad powder, cement, and limestone powder. That is, for the materials evaluated and the laboratory test conditions considered, limestone mineral powder was found to be the most moisture-sensitive filler material, whilst slaked (hydrated) lime was the most moisture-tolerant and water-damage resistant filler material

Efficiently Enhanced Selectivity of Electrocatalyzing Ethanol to High Value-Added Acetaldehyde Through Tuning the Cobalt Valence State

Using electrochemical oxidation of alcohols to substitute the oxygen evolution reaction is beneficial to reduce the energy consumption of hydrogen production. Converting alcohols into high value-added products with high efficiency and selectivity by designing a proper electrocatalyst is economical and has promising applications. In this work, two types of spinel cubic phase Co3O4 with different contents of oxygen vacancies were obtained by annealing the same precursor in air and argon gas atmosphere, respectively. The results of X-ray photoelectron spectroscopy and in situ Raman spectra reveal that abundant Co4+ sites were formed on the surface of Co3O4–air under the electrocatalysis condition, while main Co3+ sites were formed on the surface of Co3O4–Ar. The electrocatalytic ethanol oxidation tests and density functional theory calculation reveal that the Co4+ sites exhibit more proper adsorption energy to the O*CCH3 intermediate, which benefits the formation of high-value-added acetaldehyde products instead of common acetic acid products with a higher degree of oxidation. The Faradaic efficiency of the Co3O4–air catalyst to acetaldehyde achieves 60.02%, and the selectivity to acetaldehyde reaches 79.63% at an oxidation overpotential of 1.46 V. This work provides the possibility and guidance for electrochemical oxidation of alcohols into high value-added products

Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories

The rapid development of light detection and ranging (LiDAR) techniques is advancing ecological and forest research. During the last decade, numerous single tree segmentation techniques have been developed using airborne LiDAR data. However, accurate crown segmentation using terrestrial or mobile LiDAR data, which is an essential prerequisite for extracting branch level forest characteristics, is still challenging mainly because of the difficulties posed by tree crown intersection and irregular crown shape. In the current work, we developed a comparative shortest-path algorithm (CSP) for segmenting tree crowns scanned using terrestrial (T)-LiDAR and mobile LiDAR. The algorithm consists of two steps, namely trunk detection and subsequent crown segmentation, with the latter inspired by the well-proved metabolic ecology theory and the ecological fact that vascular plants tend to minimize the transferring distance to the root. We tested the algorithm on mobile-LiDAR-scanned roadside trees and T-LiDAR-scanned broadleaved and coniferous forests in China. Point-level quantitative assessments of the segmentation results showed that for mobile-LiDAR-scanned roadside trees, all the points were classified to their corresponding trees correctly, and for T-LiDAR-scanned broadleaved and coniferous forests, kappa coefficients ranging from 0.83 to 0.93 were obtained. We believe that our algorithm will make a contribution to solving the problem of crown segmentation in T-LiDAR scanned-forests, and might be of interest to researchers in LiDAR data processing and to forest ecologists. In addition, our research highlights the advantages of using ecological theories as guidelines for processing LiDAR data. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved