Yanbing Liu

Alternative stress effects on Ca2+ localization in Chrysanthemum callus cells

The effects of alternative stress on Ca2+ distribution in Chrysanthemum callus cells were studied. The field of alternative stress was generated through a strong sound field system in our lab, and the changes of Ca2+ distribution in subcellular structures were observed by electron microscopy (EM). We found that there were distinct differences between the control and treatment group, In the control cells, Ca2+ was concentrated to vacuole and was less in other organelles, while in the treatment samples, Ca2+ was concentrated to the vacuole membrane with a linear pattern, less Ca2+ was in the vacuoles. Ca2+ increased notably in cytoplasm, inner lateral of vacuole membrane and nucleus. Some Ca2+ were found in the Golgi complex and chloroplast. In this paper, we emphatically discussed the possible mechanisms of Ca2+ redistribution in the Chrysanthemum callus cells.

Biological effects of Actinidia chinensis callus on mechanical vibration

Studies on the relationship between plant cell (tissue) and physical stimulation is one of the study focuses of Biomechanics, and it is an effective method to research the stress effect of woody plant tissue by treating Actinidia chinensis callus with mechanical vibration. In this paper, several important plant physiological indexes were measured in order to further explore its mechanism stress effect. From our experimental results, we found that there was a great similarity of stress effect on mechanical vibration between A. chinensis and Gerbera Jamesonii acrocarpous, a kind of herbage plant, which had been studied in our laboratory before. That is to say, the mechanical vibration also has dual effects on woody plant. The proper frequency of mechanical vibration stimulating on the callus could promote callus growth, and the optimum frequency is about 3 Hz. The study showed that the biological effects of the mechanical vibration on the callus are obvious, and they are either positive or negative effects in comparison with that of the control group. The mechanism of vibration stimulation on plant tissue (cell) from the level of cell and molecule was discussed in this paper.

A novel trust-based secure data aggregation for Internet of Things

Data aggregation is an efficient technique widely used in Internet of Things (IOT) to collect statistics about data of interest. However, the resource-limited and the inherent unreliability of wireless transmission makes the sensors in the IOT are vulnerable to many attacks. Hence, how to design an efficient and secure data aggregation mechanism becomes critical. In this paper, we present TBSDA-a trust-based secure data aggregation scheme. In TBSDA, secure data aggregation is achieved by behavior-detected trust evaluation and data assembling technique. The trust evaluation and data aggregation work synergistically. We analyze the performance of TBSDA in term of communication overhead and the sensitivity of malicious nodes that behavior abnormally, and compare with other clustering scheme.

A behavioral anomaly detection strategy based on time series process portraits for desktop virtualization systems

As the application of desktop virtualization systems (DVSs) continues to gain momentums, the security issue of DVSs becomes increasingly critical and is extensively studied. Unfortunately, the majority of current researches on DVSs only focuses on the virtual machines (VMs) on the servers, and overlooks to a large extent the security issue of the clients. In addition, traditional security techniques are not completely suitable for the DVSs’ particularly thin client environment. Towards finding a solution to these problems, we propose a novel behavioral anomaly detection method for DVS clients by creating and using process portraits. Based on the correlations between users, virtualized desktop processes (VDPs), and VMs in DVSs, this proposed method describes the process behaviors of clients by the CPU utilization rates of VMs located on the server, constructs process portraits for VDPs by hidden Markov models and by considering the user profiles, and detects anomalies of VDPs by contrasting VDPs’ behaviors against the constructed process portraits. Our experimental results show that the proposed method is effective and successful.

Analyzing, Modeling, and Simulation for Human Dynamics in Social Network

This paper studies the human behavior in the top-one social network system in China (Sina Microblog system). By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time) obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements: social pressure, social identity, social participation, and social relation between individuals. Empirical results present the four elements impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.

A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM) migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM), the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

A Novel Behavior-Based Virus Detection Method for Smart Mobile Terminals

The security of smart mobile terminals has been an increasingly important issue in recent years. While there are extensive researches on virus detections for smart mobile terminals, most of them share the same framework of virus detection as that for personal computers, and few of them tackle the problem from the standpoint of detection methodology. In this paper, we propose a behavior-based virus detection method for smart mobile terminals which signals the existence of malicious code through identifying the anomaly of user behaviors. We first propose a model to collect and analyze user behaviors and then present a polynomial time algorithm for the virus detection. Next, we evaluate this algorithm by testing it with two commercial malwares and one malware written by ourselves and show that our algorithm enjoys a high virus detection rate. Finally, we notice that the rate of change of the virus detection rate of the algorithm with respect to thresholds matches the real-world situation of user behaviors, which indicates that the proposed algorithm is feasible.

A Hybrid Human Dynamics Model on Analyzing Hotspots in Social Networks

The increasing development of social networks provides a unique source nfor analyzing human dynamics in the modern age. In this paper, we analyze nthe top-one Internet forum in China (“Tianya Club”) and identify the statistical properties of hotspots, which can promptly reflect the crowd events nin peoples real-life. Empirical observations indicate that the interhotspot ndistribution follows a power law. To further understand the mechanism of nsuch dynamic phenomena, we propose a hybrid human dynamic model that ncombines “memory” of individual and “interaction” among people. To build na rich simulation and evaluate this hybrid model, we apply three different nnetwork datasets (i.e., WS network, BA network, and Karate-Club). Our nsimulation results are consistent with the empirical studies, which indicate nthat the model can provide a good understanding of the dynamic mechanism nof crowd events using such social networking data. We additionally analyze nthe sensitivity of model parameters and find the optimal model settings.