In the classical computation theory, the language of a system features the computational behavior of the system but it does not distinguish the determinism and nondeterminism of actions. However, Milner found that the determinism and nondeterminism affect the interactional behavior of interactive systems and thus the notion of language does not features the interactional behavior. Therefore, Milner proposed the notion of (weak) bisimulation to solve this problem. With the development of internet, more and more interactive systems occur in the world, such as electronic trading system. Security is one of the most important topics for these systems. We find that different security policies can also affect the interactional behavior of a system, which exactly is the reason why a good policy can strengthen the security. In other words, two interactive systems with different security policies are not of an equivalent behavior although their functions (or business processes) are identical. However, the classic (weak) bisimulation theory draws an opposite conclusion that their behaviors are equivalent. The notion of (weak) bisimulation is not suitable for these security-oriented interactive systems since it does not consider a security policy. This paper proposes the concept of secure bisimulation in order to solve the above problem.

A parallel multiplexing scheduling (PMS) scheme is proposed for distributed antenna systems (DAS), which greatly improves average system throughput due to multi-user diversity and multi-user multiplexing. However, PMS has poor fairness because of the use of the "best channel selection" criteria in the scheduler. Thus we present a parallel proportional fair scheduling (PPFS) scheme, which combines PMS with proportional fair scheduling (PFS) to achieve a tradeoff between average throughput and fairness. In PPFS, the "relative signal to noise ratio (SNR)" is employed as a metric to select the user instead of the "relative throughput" in the original PFS. And only partial channel state information (CSI) is fed back to the base station (BS) in PPFS. Moreover, there are multiple users selected to transmit simultaneously at each slot in PPFS, while only one user occupies all channel resources at each slot in PFS. Consequently, PPFS improves fairness performance of PMS greatly with a relatively small loss of average throughput compared to PFS.

Both multiplexing and multi-user diversity are exploited based on Round Robin (RR) scheduling to achieve tradeoffs between average throughput and fairness in distributed antenna systems (DAS). Firstly, a parallel Round Robin (PRR) scheduling scheme is presented based on the multi-user multiplexing in spatial domain to enhance the throughput, which inherits the excellent fairness performance of RR. Then a parallel grouping Round Robin (PGRR) is proposed to exploit multi-user diversity based on PRR. Due to the integration of multi-user diversity and multi-user multiplexing, a great improvement of throughput is achieved in PGRR. However, the expense of the improvement is at the degradation of fairness since the "best channel criteria" is used in PGRR. Simulations verify analysis conclusions and show that tradeoffs between throughput and fairness can be achieved in PGRR.

Compressive sensing (CS) shows that a sparse or compressible signal can be exactly recovered from its linear measurements at a rate significantly lower than the Nyquist rate. As an extreme case, 1-bit compressive sensing (1-bit CS) states that an original sparse signal can be recovered from the 1-bit measurements. In this paper, we intrduce a Fast and Accurate Two-Stage (FATS) algorithm for 1-bit compressive sensing. Simulations show that FATS not only significantly increases the signal reconstruction speed but also improves the reconstruction accuracy.

Petri nets are a kind of formal language that are widely applied in concurrent systems associated with resource allocation due to their abilities of the natural description on resource allocation and the precise characterization on deadlock. Weighted System of Simple Sequential Processes with Resources (WS3PR) is an important subclass of Petri nets that can model many resource allocation systems in which 1) multiple processes may run in parallel and 2) each execution step of each process may use multiple units from a single resource type but cannot use multiple resource types. We first prove that the liveness problem of WS3PR is co-NP-hard on the basis of the partition problem. Furthermore, we present a necessary and sufficient condition for the liveness of WS3PR based on two new concepts called Structurally Circular Wait (SCW) and Blocking Marking (BM), i.e., a WS3PR is live iff each SCW has no BM. A sufficient condition is also proposed to guarantee that an SCW has no BM. Additionally, we show some advantages of using SCW to analyze the deadlock problem compared to other siphon-based ones, and discuss the relation between SCW and siphon. These results are valuable to the further research on the deadlock prevention or avoidance for WS3PR.

Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods.

Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.