Description:
Summary Trophic cascade theory predicts that apex predators structure ecosystems by regulating mesopredator and herbivore abundance and behaviour. Studies on trophic cascades have typically focused on short linear chains of species interactions. A framework that integrates more realistic and complex interactions is needed to make broader predictions on ecosystem structuring. Network analysis is used to study food webs and other types of species interaction networks. These often comprise large numbers of species but rarely account for multiple interaction types and strengths. Here, we develop an intermediate complexity theoretical framework that allows specification of multiple interaction types and strengths for the study of trophic cascades. This ecological network is designed to suit data typically derived from field‐based studies. The trophic cascade network contains fewer nodes than food webs, but provides semi‐weighted directional links that enable different types of interactions to be included in a single model. We use this trophic cascade network model to explore how an apex predator shapes ecosystem structure in an Australian arid ecosystem. We compared two networks that contrasted in the dominance of an apex predator, the dingo (Canis dingo), using published results ranking the direction and strength of key interactions. Nodes and links interacted dynamically to shape these networks. We examined how changes to an apex predator population affect ecosystem structure through their direct and indirect influences on different components of this ecological community. Under strong apex predator influence, the network structure was denser and more complex, even and top‐down driven and dingo predation and soil commensalism formed denser interactive modules. Under weak apex predator influence (e.g. reflecting predator control), the resulting network structure was frayed, with mesopredator predation and grazing forming modules. Our study demonstrates that networks of intermediate complexity can provide a powerful tool for elucidating potential ecosystem‐wide effects of apex predators and predicting the consequences of management interventions such as predator control. Integrating trophic cascades, with their array of complex interactions, with the three‐dimensional structure of ecological networks, has the potential to reveal ‘ecological architecture’ that neither captures on its own.
Description:
Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
Description:
Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
Description:
With the emergence of Wireless Multimedia Sensor Networks (WMSNs), the distribution of multimedia contents have now become a reality. Without proper management, the transmission of multimedia data over WMSNs affects the performance of networks due to excessive packet-drop. The existing studies on Quality of Service (QoS) mostly deal with simple Wireless Sensor Networks (WSNs) and as such do not account for an increasing number of sensor nodes and an increasing volume of data. In this paper, we propose a novel framework to support QoS in WMSNs along with a light-weight Error Concealment (EC) scheme. The EC schemes play a vital role to enhance Quality of Experience (QoE) by maintaining an acceptable quality at the receiving ends. The main objectives of the proposed framework are to maximize the network throughput and to cover-up the effects produced by dropped video packets. To control the data-rate, Scalable High efficiency Video Coding (SHVC) is applied at multimedia sensor nodes with variable Quantization Parameters (QPs). Multi-path routing is exploited to support real-time video transmission. Experimental results show that the proposed framework can efficiently adjust large volumes of video data under certain network distortions and can effectively conceal lost video frames by producing better objective measurements.
Description:
An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for a long-term usage. These nodes are capable of capturing both multimedia and nonmultimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, underlying routing protocols need to provide an acceptable level of Quality of Service (QoS) support for multimedia traffic. In this paper, we propose a Seamless and Authorized Streaming (SAMS) framework for a cluster-based hierarchical WMSN. The SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their Cluster Heads (CHs). Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its CH. This waiting may result in an excessive packet-loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for an intercluster communication. In the case of a buffer overflow, a member node in one cluster switches to a neighboring CH provided that the latter has an available channel for allocation. The experimental results show that the SAMS provides an acceptable level of QoS and enhances security of an underlying network.