A Ni-catalyzed C–H/C–H cross-dehydrogenative coupling (CDC) reaction was developed for constructing various highly functionalized alkyl (aryl)-substituted thiophenes.
As the developments of new techniques, mobile social networks have been built wildly. To obtain and spread information over mobile social networks efficiently, the influence maximization problem is to find a seed nodes set with limited size such that it can influence as many nodes as possible. Previous works ignore the dynamic influence phenomenon of diffusing information on mobile social networks. In this article, we propose a new model to express the procedure of diffusing information under the existence of dynamic influence. Theoretical analysis shows that the influence maximization problem under new model is non-deterministic polynomial-time hard, and efficient approximation algorithm is proposed. Experimental studies on real data sets show that the new model can process dynamic influence well in the diffusing information procedure, and the proposed algorithms can solve the influence maximization problem on new model efficiently.
Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance parameters of the models can depend on the explanatory variable, and these are called heterogeneous semiparametric spatial autoregressive models. In order to estimate the model parameters, a Bayesian estimation method is proposed for heterogeneous SSAR models based on B-spline approximations of the nonparametric function. Then, we develop an efficient Markov chain Monte Carlo sampling algorithm on the basis of the Gibbs sampler and Metropolis–Hastings algorithm that can be used to generate posterior samples from posterior distributions and perform posterior inference. Finally, some simulation studies and real data analysis of Boston housing data have demonstrated the excellent performance of the proposed Bayesian method.
Web queries tend to have multiple user intents. Automatically identifying query intents will benefit search result navigation, search result diversity and personalized search. This paper presents the HITSCIR system in NTCIR-9 subtopic mining task. Firstly, the system collects query intent candidates from multiple resources. Secondly, Affinity Propagation algorithm is applied for clustering these query intent candidates. It could decide the number of clusters automatically. Each cluster has a representative intent candidate called exemplar. Prior preference and heuristic pair-wise preferences could be incorporated in the clustering framework. Finally, the exemplars are ranked by considering each own quality and the popularity of the clusters they represent. The NTCIR-9 evaluation results show that our system could effectively mine query intents with good relevance, diversity and readability.
Bad data injection is one of most dangerous attacks in smart grid, as it might lead to energy theft on the end users and device breakdown on the power generation. The attackers can construct the bad data evading the bad data detection mechanisms in power system. In this paper, a novel method, named as Adaptive Partitioning State Estimation (APSE), is proposed to detect bad data injection attack. The basic ideas are: 1) the large system is divided into several subsystems to improve the sensitivity of bad data detection; 2) the detection results are applied to guide the subsystem updating and re-partitioning to locate the bad data. Two attack cases are constructed to inject bad data into an IEEE 39-bus system, evading the traditional bad data detection mechanism. The experiments demonstrate that all bad data can be detected and located within a small area using APSE.
Capturing the anomalies of a cyber system in power control networks would promote operational awareness. Correlation of such events, e.g., intrusion attempts, traffic flow, and other signatures, together with control alarm events gives operators an in-depth understanding in order to make an informed decision. This paper proposes a threat inference framework to promote real-time vulnerability assessment associated with cyber intrusions on power communication networks. Wasserstein Generative Adversarial Networks (WGAN) is proposed to estimate the performance of the adversarial model. Additionally, a machine-learning framework is introduced to model the filtering process of the security devices, i.e., firewalls, isolation, and encryption devices, and the posterior fitting method is incorporated to establish an accurate probabilistic formulation. Finally, a testbed is established to coordinate system evaluation. Verification of the intrusion model is part of the implementation to quantify system risks based on the anomalies using (1) the open-source emulator, and (2) an externally imported system analyzer to characterize resulting impacts. The effectiveness and feasibility of the generative models are verified in a comparison study where the proper parameter settings could be obtained. The proposed framework is justified with extensive studies of substation networks using real-world settings.
This research studies the speech recognition process, and divides the speech recognition of linear system into four steps – speech acquisition, training, classification and results. For each part, its optimization is given. First, the effects of different feature sets of the same speech on classification results were tested. Then optimal parameter values of the neural network are found. Second, test the effect of different speech signal processing methods on speech recognition results. Present an analysis that shows whether STFT and ASTFT processing methods are effective in reducing error rate. Modify a neural network with four outputs to classify more digits. Third, the training step was modified from 10 outputs to 4 outputs (decimal to binary) and nCCs were transferred to binary for optimizing.
With the advent of multicore processors, there is a great need to write parallel programs to take advantage of parallel computing resources. However, due to the nondeterminism of parallel execution, the malware behaviors sensitive to thread scheduling are extremely difficult to detect. Dynamic taint analysis is widely used in security problems. By serializing a multithreaded execution and then propagating taint tags along the serialized schedule, existing dynamic taint analysis techniques lead to under-tainting with respect to other possible interleavings under the same input. In this paper, we propose an approach called DSTAM that integrates symbolic analysis and guided execution to systematically detect tainted instances on all possible executions under a given input. Symbolic analysis infers alternative interleavings of an executed trace that cover new tainted instances, and computes thread schedules that guide future executions. Guided execution explores new execution traces that drive future symbolic analysis. We have implemented a prototype as part of an educational tool that teaches secure C programming, where accuracy is more critical than efficiency. To the best of our knowledge, DSTAM is the first algorithm that addresses the challenge of taint analysis for multithreaded program under fixed inputs.
This article describes our novel approach to the automated detection and analysis of metaphors in text. We employ robust, quantitative language processing to implement a system prototype combined with sound social science methods for validation. We show results in 4 different languages and discuss how our methods are a significant step forward from previously established techniques of metaphor identification. We use Topical Structure and Tracking, an Imageability score, and innovative methods to build an effective metaphor identification system that is fully automated and performs well over baseline.
The binary-level function matching has been widely used to detect whether there are 1-day vulnerabilities in released programs. However, the high false positive is a challenge for current function matching solutions, since the vulnerable function is highly similar to its corresponding patched version. In this paper, the Binary X-Ray (BinXray), a patch based vulnerability matching approach, is proposed to identify the specific 1-day vulnerabilities in target programs accurately and effectively. In the preparing step, a basic block mapping algorithm is designed to extract the signature of a patch, by comparing the given vulnerable and patched programs. The signature is represented as a set of basic block traces. In the detection step, the patching semantics is applied to reduce irrelevant basic block traces to speed up the signature searching. The trace similarity is also designed to identify whether a target program is patched. In experiments, 12 real software projects related to 479 CVEs are collected. BinXray achieves 93.31% accuracy and the analysis time cost is only 296.17ms per function, outperforming the state-of-the-art works.
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