Spammers, who manipulate online reviews to promote or suppress products, are flooding in online commerce. To combat this trend, there has been a great deal of research focused on detecting review ...spammers, most of which design diversified features and thus develop various classifiers. The widespread growth of crowdsourcing platforms has created largescale deceptive review writers who behave more like normal users, that the way they can more easily evade detection by the classifiers that are purely based on fixed characteristics. In this paper, we propose a hybrid semisupervised learning model titled hybrid PU-learning-based spammer detection (hPSD) for spammer detection to leverage both the users' characteristics and the user-product relations. Specifically, the hPSD model can iteratively detect multitype spammers by injecting different positive samples, and allows the construction of classifiers in a semisupervised hybrid learning framework. Comprehensive experiments on movie dataset with shilling injection confirm the superior performance of hPSD over existing baseline methods. The hPSD is then utilized to detect the hidden spammers from real-life Amazon data. A set of spammers and their underlying employers (e.g., book publishers) are successfully discovered and validated. These demonstrate that hPSD meets the real-world application scenarios and can thus effectively detect the potentially deceptive review writers.
We combine two amazing abilities found in nature: the superhydrophobic property of lotus leaf and the adhesive ability of mussel adhesive protein. The molecular structure mimic of the single units of ...adhesive proteins, dopamine, was polymerized in an alkaline aqueous solution to encapsulate microparticles. The as-formed thin polydopamine walls worked as reactive templates to generate silver nanoparticles on the capsuled particles. As a result, core/shell/satellite composite particles were generated with a hierarchical structure similar to the micromorphology of lotus leaf. The composite particles exhibited extremely water repellence after fluorination. Because dopamine can deposit and adhere to all kinds of materials, this method can be applied to diverse microparticles, from organic to inorganic. In addition, particles of different sizes and matters can be modified to superhydrophobic particles in one pot. Magnetic particles have also been prepared which could be used as oil-absorbent and magnetic controlled carriers. “Oil marbles” formed underwater were achieved for the first time.
We have developed a miniature two-photon microscope equipped with an axial scanning mechanism and a long-working-distance miniature objective to enable multi-plane imaging over a volume of 420 × 420 ...× 180 μm
at a lateral resolution of ~1 μm. Together with the detachable design that permits long-term recurring imaging, our miniature two-photon microscope can help decipher neuronal mechanisms in freely behaving animals.
Classification of land use and land cover from remote sensing images has been widely used in natural resources and urban information management. The variability and complex background of land use in ...high-resolution imagery poses greater challenges for remote sensing semantic segmentation. To obtain multi-scale semantic information and improve the classification accuracy of land-use types in remote sensing images, the deep learning models have been wildly focused on. Inspired by the idea of the atrous-spatial pyramid pooling (ASPP) framework, an improved deep learning model named RAANet (Residual ASPP with Attention Net) is constructed in this paper, which constructed a new residual ASPP by embedding the attention module and residual structure into the ASPP. There are 5 dilated attention convolution units and a residual unit in its encoder. The former is used to obtain important semantic information at more scales, and residual units are used to reduce the complexity of the network to prevent the disappearance of gradients. In practical applications, according to the characteristics of the data set, the attention unit can select different attention modules such as the convolutional block attention model (CBAM). The experimental results obtained from the land-cover domain adaptive semantic segmentation (LoveDA) and ISPRS Vaihingen datasets showed that this model can enhance the classification accuracy of semantic segmentation compared to the current deep learning models.
For lack of temporal and spatial pattern of phytoplankton community structure in the Yellow Sea during the Holocene,biomarker records in core CO2 and NO5 were used to reconstruct the phytoplankton ...community structures,combined with published biomarker records.In the early Holocene,the relative ratios of alkenones(A/ΣPB)and brassicasterol(B/ΣPB)were low,while the relative ratio of dinosterol(D/ΣPB)was high.High value of TMBR’index indicates that the phytoplankton community structure was controlled by terrestrial nutrients during this period.In the mid Holocene,A/ΣPB increased,while B/ΣPB and D/ΣPB decreased.This is attributed to the Yellow Sea Warm Current intrusion with high temperature and high salinity.A/ΣPB increased significantly at core sites ZY3,ZY2,ZY1 and YE-2(35.5°N zone),while slightly at core sites CO2 and NO5.The Yellow Sea Warm Current flowed through the 35.5°N zone,controlling the phytoplankton community structure in the zone.However,the phytoplankton community structure in site CO2 and NO5 was
Stall flow patterns occur frequently in pump turbines under off-design operating conditions. These flow patterns may cause intensive pressure pulsations, sudden increases in the hydraulic forces of ...the runner, or other adverse consequences, and are some of the most notable subjects in the study of pump turbines. Existing methods for identifying stall flow patterns are not, however, sufficiently objective and accurate. In this study, a convolutional neural network (CNN) is built to identify and analyze stall flow patterns. The CNN consists of input, convolutional, downsampling, fully connected, and output layers. The runner flow field data from a model pump–turbine are simulated with three-dimensional computational fluid dynamics and part of the classifiable data are used to train and test the CNN. The testing results show that the CNN can predict whether or not a blade channel is stalled with an accuracy of 100%. Finally, the CNN is used to predict the flow status of the unclassifiable part of the simulated data, and the correlation between the flow status and the relative flow rate in the runner blade channel is analyzed and discussed. The results show that the CNN is more reliable in identifying stall flow patterns than using the existing methods.
Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, ...external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a study of 11 widely used internal clustering validation measures for crisp clustering. The results of this study indicate that these existing measures have certain limitations in different application scenarios. As an alternative choice, we propose a new internal clustering validation measure, named clustering validation index based on nearest neighbors (CVNN), which is based on the notion of nearest neighbors. This measure can dynamically select multiple objects as representatives for different clusters in different situations. Experimental results show that CVNN outperforms the existing measures on both synthetic data and real-world data in different application scenarios.
Sepsis is the primary cause of acute kidney injury (AKI) and is associated with high mortality rates. Growing evidence suggests that noncoding RNAs are vitally involved in kidney illnesses, whereas ...the role of circular RNAs (circRNAs) in sepsis-induced AKI (SAKI) remains largely unknown. In this present study, caecal ligation and puncture (CLP) in mice was performed to establish an SAKI model. The expression of circRNAs and mRNAs was analysed using circRNA microarray or next-generation sequencing. The results revealed that the expressions of 197 circRNAs and 2509 mRNAs were dysregulated. Validation of the selected circRNAs was performed by qRT-PCR. Bioinformatics analyses and chromatin immunoprecipitation demonstrated that NF-κB/p65 signalling induced the upregulation of circC3, circZbtb16, and circFkbp5 and their linear counterparts by p65 transcription in mouse tubular epithelial cells (mTECs). Furthermore, competitive endogenous RNA (ceRNA) networks demonstrated that some components of NF-κB signalling were potential targets of these dysregulated circRNAs. Among them, Tnf-α was increased by circFkbp5 through the downregulation of miR-760-3p in lipopolysaccharide (LPS)-stimulated mTECs. Knocking down circFkbp5 inhibited the p65 phosphorylation and apoptosis in injured mTECs. These findings suggest that the selected circRNAs and the related ceRNA networks provide new knowledge into the fundamental mechanism of SAKI and circFkbp5/miR-760-3p/Tnf-α axis might be therapeutic targets.
With the geosynchronous synthetic aperture radar (SAR) satellite as the transmitter, the unmanned aerial vehicle (UAV) can passively receive the echo within the illuminated ground area and achieve ...2-D imaging of the interested target. This SAR system, known as GEO-UAV bistatic SAR, is capable of autonomously accomplishing the bistatic SAR mission in rough terrain environments by prespecifying a path for the UAV receiver. In this paper, the GEO-UAV bistatic SAR system is first investigated. The practical advantages and spatial resolution are then analyzed in detail. The spatial resolution of GEO-UAV bistatic SAR is dependent on the observation geometry, which is determined by the UAV path. Therefore, the path planning for GEO-UAV bistatic SAR aims at identifying a set of optimal paths for the UAV receiver to travel through a 3-D terrain environment that simultaneously guarantees the safety of the UAV and achieves SAR imaging with optimized performance during the flight. The path planning is modeled as a constrained multiobjective optimization problem (MOP), which accurately represents the two main aspects for the path planning problem, i.e., UAV navigation and bistatic SAR imaging. Then, a path planning method based on a constrained-adaptive-multiobjective-differential-evolution algorithm is proposed to solve the MOP and generate multiple feasible paths for the UAV receiver with different tradeoffs between navigation for UAV and bistatic SAR imaging performance. The GEO-UAV bistatic SAR mission designer can choose a path from the solution set according to the application requirements, which makes the method more pragmatic.
