XMAM:X-raying Models with A Matrix to Reveal Backdoor Attacks for Federated Learning

Federated Learning (FL) has received increasing attention due to its privacy protection capability. However, the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks. Former researchers proposed several robust aggregation methods. Unfortunately, many of these aggregation methods are unable to defend against backdoor attacks. What's more, the attackers recently have proposed some hiding methods that further improve backdoor attacks' stealthiness, making all the existing robust aggregation methods fail. To tackle the threat of backdoor attacks, we propose a new aggregation method, X-raying Models with A Matrix (XMAM), to reveal the malicious local model updates submitted by the backdoor attackers. Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates, we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior. Specifically, like X-ray examinations, we investigate the local model updates by using a matrix as an input to get their Softmax layer's outputs. Then, we preclude updates whose outputs are abnormal by clustering. Without any training dataset in the server, the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones. For instance, when other methods fail to defend against the backdoor attacks at no more than 20% malicious clients, our method can tolerate 45% malicious clients in the black-box mode and about 30% in Projected Gradient Descent (PGD) mode. Besides, under adaptive attacks, the results demonstrate that XMAM can still complete the global model training task even when there are 40% malicious clients. Finally, we analyze our method's screening complexity, and the results show that XMAM is about 10-10000 times faster than the existing methods.Comment: 23 page

Remote sensing and social sensing data reveal scale-dependent and system-specific strengths of urban heat island determinants

Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25–5 km) and spatial extents (20–60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect

Study and Simulation of Deformation Mechanics Modeling of Flexible Workpiece Processing by Rayleigh-Ritz Method

This paper discusses the calculation problems of bending deformation of FWP processing. Take three axis CNC machining as an example, to establish mechanics model of flexible workpiece processing process. The flexible workpiece balance equation is a two-dimensional partial differential equation, to solve the problem of flexible workpiece bending deformation using Rayleigh-Ritz method and designing the test function of bending deformation of flexible workpiece. By satisfying the minimum potential energy condition of FWP processing to work out the approximate solution of bending deformation of flexible workpiece, find out the relationship between material properties of flexible piece, acting force Fz, and deformation value. Finally, the rectangle flexible workpiece which is made up of polyurethane sponge is selected as an experiment subject. The results show that the average relative deviation between theoretical value and observed value is only 5.51%. It is proved that the bending deformation test function satisfies the actual deformation calculation requirements

Galangin and Pinocembrin from Propolis Ameliorate Insulin Resistance in HepG2 Cells via Regulating Akt/mTOR Signaling

Insulin resistance has a critical role in type 2 diabetes. The aim of this study was to investigate the effect of pinobanksin, galangin, chrysin, and pinocembrin from propolis on insulin resistance. Our study shows that galangin and pinocembrin can ameliorate insulin resistance; on the contrary, pinobanksin and chrysin are ineffective. Galangin and pinocembrin treatments substantially increase glucose consumption and glycogen content by enhancing the activities of hexokinase and pyruvate kinase. Galangin treatment with 80 μM increased hexokinase and pyruvate kinase activities by 21.94% and 29.12%, respectively. Moreover, we hypothesize that galangin and pinocembrin may have a synergistic effect on the improvement of insulin resistance via Akt/mTOR signaling pathway, through distinctly upregulating the phosphorylation of IR, Akt, and GSK3β and remarkably downregulating the phosphorylation of IRS. Most notably, this is the first study to our knowledge to investigate pinocembrin about the alleviation of insulin resistance. Our results provide compelling evidence for the depth development of propolis products to ameliorate insulin resistance

IEEE Access Special Section: Security Analytics and Intelligence for Cyber Physical Systems

A Cyber Physical System (CPS) is a smart network system with actuators, embedded sensors, and processors to interact with the physical world by guaranteeing the performance and supporting real-time operations of safety critical applications. These systems drive innovation and are a source of competitive advantage in today's challenging world. By observing the behavior of physical processes and activating actions, CPS can alter its behavior to make the physical environment perform better and more accurately. By definition, CPS basically has two major components including cyber systems and physical processes. Examples of CPS include autonomous transportation systems, robotics systems, medical monitoring, automatic pilot avionics, and smart grids. Advances in CPS will empower scalability, capability, usability, and adaptability, which will go beyond the simple systems of today. At the same time, CPS has also increased cybersecurity risks and attack surfaces. Cyber attackers can harm such systems from multiple sources while hiding their identities. As a result of sophisticated threat matrices, insufficient knowledge about threat patterns, and industrial network automation, CPS has become extremely insecure. Since such infrastructure is networked, attacks can be prompted easily without much human participation from remote locations, thereby making CPS more vulnerable to sophisticated cyber-attacks. In turn, large-scale data centers managing a huge volume of CPS data become vulnerable to cyber-attacks. To secure CPS, the role of security analytics and intelligence is significant. It brings together huge amounts of data to create threat patterns, which can be used to prevent cyber-attacks in a timely fashion. The primary objective of this Special Section in IEEE A CCESS is to collect a complementary and diverse set of articles, which demonstrate up-to-date information and innovative developments in the domain of security analytics and intelligence for CPS.Non peer reviewe

Geogenic enrichment of potentially toxic metals in agricultural soils derived from black shale in northwest Zhejiang, China: Pathways to and risks from associated crops

Agricultural soils derived from black shale are typically enriched in potentially toxic metals. This is a serious problem, both in terms of the ecological environment and human health. To assess the levels of potentially toxic metals, 90 paired soil-crops samples were collected from the Anji Country, western Zhejiang province, a typical exposed black shale area in China. Concentrations and bioavailability of potentially toxic metals in the soil-crops system were measured, and the associated potential risks were further evaluated. Results showed the enrichment of potentially toxic metals (i.e. Cd, Pb, Cu, Zn and Ni) in the soil and crop samples, especially a significant accumulation of Cd. Sequential extraction data indicated that Cd in soils derived from black shale was the second most dominant element in the exchangeable fraction (mean at 33.42%) and possessed high bioavailability, whereas Pb was mostly retained in the residual fraction (mean at 76.34%) and exhibited low mobility. The total concentration as well as mobility and bioavailability of Cd were the highest in the sampled soils. This resulted in a high potential ecological risk in areas with agricultural soils derived from black shale, which could eventually jeopardize the health of local residents through various exposure pathways. Overall, our findings provide a scientific basis for developing suitable management strategies to mitigate the exposure to potentially toxic metals in high risk areas

Effect of aerobic exercise on GRP78 and ATF6 expressions in mice with non-alcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is a prevalent medical condition with an ever-growing trend. Although multiple intracellular mechanisms are involved, endoplasmic reticulum (ER) stress has been demonstrated to play a significant role in the genesis and progression. Most of the research supports the advantages of exercise for NAFLD. However, little is known about the molecular mechanism(s) that underpin the effectiveness of exercise training in NAFLD. This study aimed to identify how aerobic exercise affected hepatic ER stress in a mouse NAFLD model. In this study, the mice were fed either a standard diet (SD) or a high-fat diet (HFD) for 17 weeks. HFD mice were trained on a treadmill during the last eight weeks. All animals were tested for serum levels of biochemical assays, protein expression, and gene expression. The hematoxylin and eosin, Oil red O, and immunohistochemistry staining were also performed. The results indicated that a high-fat diet generated NAFLD, with serum lipid disruption and hepatic function impairment, and increased GRP78 and ATF6 expressions. However, aerobic training reversed the majority of these alterations. It is concluded that NAFLD appears to be associated with hepatic ER stress response, and aerobic exercise mitigates NAFLD via lowering ER stress proteins GRP78 and ATF6