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The public goods game is widely accepted as a suitable theoretical paradigm for explaining collective cooperation. In this paper, we investigate the impact of heterogeneous investments on cooperation in groups, where the investment of one player to a particular group depends on the fraction of cooperators in that group. Our research reveals that the level of cooperation is significantly promoted as the level of heterogeneity in the investments increases. By studying the payoffs of players at the boundaries of cooperative clusters, we show that the positive effect on the evolution of cooperation can be attributed to the formation of clusters that are more robust against invading defectors. The presented results sharpen our understanding of cooperation in groups that are due to heterogeneity and related asymmetric influences on game dynamics.

Pigeon-inspired optimization (PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator, and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally, which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity — HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of “exploitation” and “exploration”, so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.

The Luojia1-01 satellite provides high-resolution, high-sensitivity nighttime light data at a resolution of 130 m. To effectively use the Luojia1-01 nighttime light data for global applications, the problems of relative and absolute positioning accuracy should be solved. This paper proposes a high accuracy regional geometric processing method of nighttime light imagery. We utilized a nighttime light image matching algorithm to obtain tie points, which are used in the planar block adjustment with ground control points. Then, orthorectification of all images is implemented. Finally, we obtain the nighttime light map of China by mosaicking all the nighttime light orthoimages. According to the experimental results for 275 Luojia1-01 images, the root mean square error of the tie points is 0.983 pixels and the root mean square error of independent checkpoints is 195.491 m (less than 1.5 pixels) after the planar block adjustment. The experimental results prove the validity and feasibility of the proposed method.

In order to alleviate traffic congestion on multilayer networks, designing an efficient routing strategy is one of the most important ways. In this paper, a novel routing strategy is proposed to reduce traffic congestion on two-layer networks. In the proposed strategy, the optimal paths in the physical layer are chosen by comprehensively considering the roles of nodes’ degrees of the two layers. Both numerical and analytical results indicate that our routing strategy can reasonably redistribute the traffic load of the physical layer, and thus the traffic capacity of two-layer complex networks are significantly enhanced compared with the shortest path routing (SPR) and the global awareness routing (GAR) strategies. This study may shed some light on the optimization of networked traffic dynamics.

In web service composition (WSC), quality of service (QoS)-based web services selection has been a critical research issue, and many service selection methods have been presented aiming at resolving this issue, However, most of the existing methods ignore giving a risk evaluation for critical web services in WSC where transactional requirements often require compensation cost to ensure failure atomicity . To address this issue, we present a Risk-driven selection approach by incorporating the impact of failure risk of each participating task to reduce the average losses caused by execution failures of tasks for WSC. With our method, a kind of tree called failure causing tree is built to support risk losses evaluation for each task in composition web service (CWS). Particularly, our method is more suitable to the scientific computing area where many long tasks are often involved and easily lead to a lot of losses such as computing cost, communicating cost, when some failures occur in these executing tasks. The final experiment and evaluation further demonstrate the feasibility and efficiency of our method.

Events detection is one of the most important issues in data stream processing. Outlier event, change event and burst event are three typical types of event that need to be identified. In this paper, we explore the relationship of these three types of events, and then present a unified framework, named DSED (distributed stream events detection), to handle all of them simultaneously. Rather than collecting all the data in one coordinator node for centralized processing, we also use pre-computing to reduce the communication cost for events detection. Experiment results verify the efficiency of DSED, and also show that our proposed framework can dramatically reduce the communication cost with little false position.

Some burgeoning web applications, such as web search engines, need to track, store and analyze massive real-time users' access logs with high availability of 24*7. The traditional high availability approaches towards general-purpose transaction applications are usually not efficient or economical enough to store these high-rate insertion-only archived stream data. This paper proposes RAIDB5 (Redundant Array of Independent DataBases level 5) to store and manage these archived streams. We design the read-optimized and write-optimized algorithms which is distinct from the read-optimized ones in RAID5. These algorithms significantly improve the performance of write operations via classifying the data files of databases into live and history files. Based on a Markov model, we compare the performance, availability and cost of RAIDB5 with other traditional database backup modes. The analysis results show that RAIDB5 has better performance at the same cost and has the better performability and lower cost at the same system scale than the classical double replication mode.