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Structure-activity relationship studies of a 1,2,4-triazolo-[3,4-b]thiadiazine scaffold, identified in an HTS campaign for selective STAT3 pathway inhibitors, determined that a pyrazole group and specific aryl substitution on the thiadiazine were necessary for activity. Improvements in potency and metabolic stability were accomplished by the introduction of an α-methyl group on the thiadiazine. Optimized compounds exhibited anti-proliferative activity, reduction of phosphorylated STAT3 levels and effects on STAT3 target genes. These compounds represent a starting point for further drug discovery efforts targeting the STAT3 pathway.

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This paper proposes a novel machine learning-based scheme for the automatic analysis of authentication and key agreement protocols. Considering the traditional formal protocol analysis schemes, their analysis accuracies depend heavily on the prior knowledge possessed by the analyst and the subjective understanding of the protocol. The rapid development of artificial intelligence in security field shows that the ideal way to get rid of the dependency is to use machine learning. Hence, we elaborately compare more than 2000 protocol analysis results and select 500 most representative ones of them to build a protocol dataset. Combining the protocol representation method of traditional schemes, these selected protocols are expressed as weight matrixes based on security components. Furthermore, a machine learningbased security analysis model is proposed to automatically find the attacks of the protocol. For now, three types of attacks against authentication and key agreement protocols can be identified based on our model. And experiment results show that it can reach almost 72% upper-bound performance. From the derivative of the accuracy curves, it can be inferred that the performance of our scheme will definitely get better as the dataset expands. Keywords Authentication protocols Á Machine learning Á Formal analysis of protocol security Á Protocol dataset Zhuo Ma and Yang Liu have contributed equally to this work.

Insulator segmentation is a critical step for automatic insulator fault diagnosis in high voltage transmission systems. Existing methods fail to segment insulators when they have a low contrast with the surroundings. Considering the unique shape and texture characteristics of insulators, a texture-and-shape based active contour model is proposed for insulator segmentation. The segmentation is achieved by evolving a curve iteratively by the texture features and shape priors. In the texture-driven curve evolution, a semi-local region descriptor is used to extract the texture features of insulators and a new convex energy functional is defined based on the extracted features with the topology-preserving term. The topology-preserving term keeps the curve's topology unchanged as the curve topology is determined by the shape template. In the shape-driven curve evolution, the shape context descriptor is used to align the shape template with the current curve. The semantic transformation between the shape template and the current curve is obtained by Procrustes analysis and then adopted to update the current curve to resemble the shape prior. The proposed method is applied to a set of images, and the experimental results confirm the efficacy and effectiveness of the proposed method for segmenting insulators in cluttered backgrounds.

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We introduce a two-stream model to use reflexive eye movements for smart mobile device authentication. Our model is based on two pre-trained neural networks, iTracker and PredNet, targeting two independent tasks: (i) gaze tracking and (ii) future frame prediction. We design a procedure to randomly generate the visual stimulus on the screen of mobile device, and the frontal camera will simultaneously capture head motions of the user as one watches it. Then, iTracker calculates the gaze-coordinates error which is treated as a static feature. To solve the imprecise gaze-coordinates caused by the low resolution of the frontal camera, we further take advantage of PredNet to extract the dynamic features between consecutive frames. In order to resist traditional attacks (shoulder surfing and impersonation attacks) during the procedure of mobile device authentication, we innovatively combine static features and dynamic features to train a 2-class support vector machine (SVM) classifier. The experiment results show that the classifier achieves accuracy of 98.6% to authenticate the user identity of mobile devices.

Following the approval of the State Council, the Ministry of Education, National Development and Reform Commission and the Ministry of Finance jointly organised and implemented the Schools Modern Distance Education Project in Rural Areas from 2003 to 2007. As an important measure of Chinese government, this project is based on the need of national modernisation construction, aiming at developing rural education, deepening rural educational reform, promoting compulsory education balanced development, facilitating educational modernisation through ICT application in education and achieving great leap forward in the development of rural education.During the 5 years, 11.1 billion RMB was invested in the project; 5 billion came from central government and 6.1 billion from local government. With regard to the allocation of the investment, the Chinese central government put more in the undeveloped western rural areas. For the 12 provinces located in the developing western areas, the central government investment accounted for two-thirds of the total amount, the remaining third was provided by local government. For the 11 provinces located in the more developed central areas, only one-third of the investment was from central government with two-thirds from local government. Through installation of DVD display stations, digital satellite-receiving sites and computer classrooms in rural elementary and secondary schools, the project enables billions of rural students to share high-quality instructional resources with urban students under the same blue sky. An extensive update of hardware and software facilities in rural areasBy systematically arranging-starting from several important pivotal points with diffusion to the surrounding area, and then gradually expanding to cover all regions, the Schools Modern Distance Education Project in Rural Areas has already constructed a distance education network that benefits rural primary and secondary student nationwide. By the end of 2007, the project had installed 440 142 instructional DVD players, 264 905 sets of digital satellite-receiving sites and 40858 computers classrooms, covering nearly all rural elementary and secondary schools. The condition and environ-