To assess the effect of relative humidity (RH) on drying-induced damage in concrete, the non-uniform strains and microcracks in concrete under different RH conditions were obtained using the digital image correlation (DIC) technique and lattice fracture model. The simulated non-uniform displacements were consistent with those captured using DIC. A new damage index was proposed by considering all the subsets with equivalent strain larger than the threshold tensile strength. The calculated damage index showed good correlation with the microcracks' total area and indicated that RH equal or lower than 55% could cause relatively high cracking risk. This work provides an attractive method for quantifying drying-induced damage in concrete using the DIC technique.@en

Non-uniform stresses, strains and microcracking of the concretes with three coarse aggregate sizes (5–10 mm, 10–16 mm, 16–20 mm) dried under 40% relative humidity (RH) for 60 days were quantified using digital image correlation and lattice fracture modelling. The influencing mechanism of coarse aggregate size on the drying-induced microcracking of concrete was clarified: (1) As the coarse aggregate size decreases, propagation paths of microcracking are increased, which increase the number of small microcracks and release the drying shrinkage force from mortar phase. (2) Tensile stress shells surrounding the coarse aggregates become thinner, thereby decreasing the area of large microcracks. As the coarse aggregate size decreased from 16-20 mm to 5–10 mm, the average thickness of tensile stress shells decreased from 2.13 mm to 1.09 mm at the beginning of drying, and the area of the microcracks >5 μm in width decreased from 796.6 mm2/m2 to 340.2 mm2/m2 at 60 days since drying.@en

Concrete generally deforms and cracks in a non-uniform manner under drying-induced stress. This study used the lattice fracture model to simulate the drying-induced non-uniform deformations, stresses, and micro-crack propagation in concrete. Experiments were designed to validate the lattice fracture model, wherein the drying-induced non-uniform deformations and micro-crack patterns in concrete were measured using a digital image correlation technique and a fluorescent epoxy impregnation method, respectively. It was found that the simulated non-uniform deformations and micro-crack patterns were close to the experimental observations. The interaction mechanism between drying-induced non-uniform stresses and micro-cracks was analysed based on the validated lattice fracture model. The micro-cracks were found to cause stress concentration both in coarse aggregate and the mortar that covered coarse aggregate, which could lead to high micro-cracking risk as drying continues.@en

Rumor spreading in online social networks can inflict serious damages on individual, organizational, and societal levels. This problem has been addressed via computational approach in recent years. The dominant computational technique for the identification of rumors is the binary classification that uses rumor and non-rumor for the training. In this method, the way of annotating training data points determines how each class is defined for the classifier. Unlike rumor samples that often are annotated similarly, non-rumors get their labels arbitrarily based on annotators' volition. Such an approach leads to unreliable classifiers that cannot distinguish rumor from non-rumor consistently. In this paper, we tackle this problem via a novel classification approach called one-class classification (OCC). In this approach, the classifier is trained with only rumors, which means that we do not need the non-rumor data points at all. For this study, we use two primary Twitter data sets in this field and extract 86 features from each tweet. We then apply seven one-class classifiers from three different paradigms and compare their performance. Our results show that this approach can recognize rumors with a high level of F1-score. This approach may influence the predominant mentality of scholars about computational rumor detection and puts forward a new research path toward dealing with this problem.@en

Rumor spreading in online social networks can inflict serious damages on individual, organizational, and societal levels. This problem has been addressed via computational approach in recent years. The dominant computational technique for the identification of rumors is the binary classification that uses rumor and non-rumor for the training. In this method, the way of annotating training data points determines how each class is defined for the classifier. Unlike rumor samples that often are annotated similarly, non-rumors get their labels arbitrarily based on annotators' volition. Such an approach leads to unreliable classifiers that cannot distinguish rumor from non-rumor consistently. In this paper, we tackle this problem via a novel classification approach called one-class classification (OCC). In this approach, the classifier is trained with only rumors, which means that we do not need the non-rumor data points at all. For this study, we use two primary Twitter data sets in this field and extract 86 features from each tweet. We then apply seven one-class classifiers from three different paradigms and compare their performance. Our results show that this approach can recognize rumors with a high level of F1-score. This approach may influence the predominant mentality of scholars about computational rumor detection and puts forward a new research path toward dealing with this problem.@en

To date cloud computing may provide considerable storage and computational power for cloud-based applications to support cryptographic operations. Due to this benefit, attribute-based keyword search (ABKS) is able to be implemented in cloud context in order to protect the search privacy of data owner/user. ABKS is a cryptographic primitive that can provide secure search services for users but also realize fine-grained access control over data. However, there have been two potential problems that prevent the scalability of ABKS applications. First of all, most of the existing ABKS schemes suffer from the outside keyword guessing attack (KGA). Second, match privacy should be considered while supporting multi-keyword search. In this paper, we design an efficient method to combine the keyword search process in ABKS with inner product encryption and deploy several proposed techniques to ensure the flexibility of retrieval mode, the security and efficiency of our scheme. We later put forward an attribute-based conjunctive keyword search scheme against outside KGA to solve the aforementioned problems. We provide security notions for two types of adversaries and our construction is proved secure against chosen keyword attack and outside KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the results of the simulation show that our scheme achieves stronger security without yielding significant cost of storage and computation. @en