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the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the `new' and `comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content. A series of experiments tested on public datasets demonstrate that: (1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; (2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.

CPU cache misses and branch mispredictions waste CPU cycles and affect program performance. Such software inefficiencies could be neither eliminated by existing compilers nor avoided by developers. In this paper, we propose a novel approach, named PERDICE, to automatically discover such performance bugs by leveraging concolic execution. PERDICE adopts a new path exploration algorithm to discover such software inefficiencies. In particular, we measure performance losses in the granularity of program locations (e.g., instructions, source code lines) instead of paths to avoid getting stuck into the code without software inefficiencies. Moreover, when scoring test inputs, our new approach prefers the test inputs incurring increments in performance losses. This strategy allows PERDICE to avoid getting stuck into the software inefficiencies that have been found. We have implemented PERDICE for both PC (X86 instructions) and Android smartphones (ARM instructions). The experimental results with real-world desktop software and Android native code show that PERDICE outperforms the other four popular algorithms and PROFs (a multi-path performance profiler) in terms of the speed to discover software inefficiencies and the severity (i.e, amount of wasted CPU cycles) of inefficiencies.

We design an induction current acquisition device based on ARM in order to realize high resolution and high sampling rate of acquisition for the induction current in wire-loop. Considering its characteristics of fast attenuation and small signal amplitude, we use the method of multi-path fusion for noise suppression. In the paper, the design is carried out from three aspects of analog circuit and device selection, independent power supply structure and the electromagnetic interference suppression of high frequency. DMA and ping-pong buffer, as a new data transmission technology, solves real time storage problem of massive data. The performance parameters of ARM acquisition device are tested. The comparison test of ARM acquisition device and cRIO acquisition device is performed at different time constants. The results show that it has 120dB dynamic range, 47kHz bandwidth, 96kHz sampling rate, 5μV the smallest resolution, and its average error value is not more than 4%, which proves the high accuracy and stability of the device.

Dynamic symbolic execution is an important automated testing technique. Firstly, we introduce the traditional symbolic execution and dynamic symbolic execution technology, and then review the research status of Python application testing with dynamic symbolic execution technology. Then we compare and analyze the dynamic symbolic execution tools with the existing Python programs and Architectures, performance, and supported data types. Finally, the development direction of this field prospects. It is helpful for researchers to understand the progress of dynamic symbolic execution technology in current Python program and lay a foundation for future research work.

Aiming to solve a series of problems in photo collection over citizen’s license, this paper proposes Portrait Extraction Algorithm over our face based on facial detection technology and state-of-the-art image segmentation algorithm. Considering an input image where the foreground stands a man with unfixed size and its background is all sorts of complicated background, firstly we use Haar&Adaboost facial detection algorithm as a preprocessing method so as to divide the image into different sub-systems, and we get a fix-sized image of human face. Then we use GrabCut and closed-form algorithm to segment the preprocessed image and output an image which satisfies our requirements (i.e. the fixed size and fixed background). Up to now the GrabCut and closed-form algorithm has been realized, both of which have its own advantages and shortages.

Study on diffusion behaviors, such as contagion of virus, adoption of production, and information propagation, is one of the hottest topics of social network research. The conventional models usually focus on a particular factor that influences the contagion process. A novel model of diffusion based on multiple behavior evidence fusion is proposed on the analysis of prevalent social network data set, which can simulate the information propagation process with the fitting goodness of 0.79. In this model, impact factors, i.e. the influence between neighborhood, and the activity of user, are taken into account together and fused into combined evidence mass function, which can be used to infer users' forwarding behavior by D-S theory reasoning. Series of experiments tested on the published Enron email data set show that the multiple evidence fusion based diffusion model (MEFDM) is reasonable and feasible in demonstrating the cascading of information in the social network.